🟢 Name Of Dataset: LIDC-IDRI
🟢 Description Of Dataset:
TheLIDC-IDRIdataset contains lesion annotations from four experienced thoracic radiologists. LIDC-IDRI contains 1,018 low-dose lung CTs from 1010 lung patients.Source:A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans
🟢 Official Homepage: https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
🟢 Number of articles that used this dataset: 237
🟢 Dataset Loaders:
Shwe234/himanshumajordataset:
https://github.com/Shwe234/himanshumajordataset
your-username/your-repository:
https://github.com/your-username/your-repository
🟢 Articles related to the dataset:
📝 UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
📝 Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
📝 Models Genesis
📝 Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
📝 nnDetection: A Self-configuring Method for Medical Object Detection
📝 A Probabilistic U-Net for Segmentation of Ambiguous Images
📝 A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
📝 Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
📝 FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
📝 Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
TheLIDC-IDRIdataset contains lesion annotations from four experienced thoracic radiologists. LIDC-IDRI contains 1,018 low-dose lung CTs from 1010 lung patients.Source:A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans
🟢 Official Homepage: https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
🟢 Number of articles that used this dataset: 237
🟢 Dataset Loaders:
Shwe234/himanshumajordataset:
https://github.com/Shwe234/himanshumajordataset
your-username/your-repository:
https://github.com/your-username/your-repository
🟢 Articles related to the dataset:
📝 UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
📝 Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
📝 Models Genesis
📝 Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
📝 nnDetection: A Self-configuring Method for Medical Object Detection
📝 A Probabilistic U-Net for Segmentation of Ambiguous Images
📝 A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
📝 Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
📝 FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
📝 Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤5
🟢 Name Of Dataset: ADNI (Alzheimer's Disease NeuroImaging Initiative)
🟢 Description Of Dataset:
Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4] firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of other initiatives related to AD and other diseases have been designed and implemented using ADNI as a model.[4] ADNI has been running since 2004 and is currently funded until 2021.[7]Source: Wikipedia, https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative
🟢 Official Homepage: http://adni.loni.usc.edu/
🟢 Number of articles that used this dataset: 28
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
📝 Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
📝 Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge
📝 Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
📝 An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications
📝 AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans
📝 The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
📝 TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data
📝 Alzheimer's Disease Brain MRI Classification: Challenges and Insights
📝 Inference of nonlinear causal effects with GWAS summary data
==================================
🔴 For more datasets resources:
✓ https://t.me/DataScienceT
🟢 Description Of Dataset:
Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4] firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of other initiatives related to AD and other diseases have been designed and implemented using ADNI as a model.[4] ADNI has been running since 2004 and is currently funded until 2021.[7]Source: Wikipedia, https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative
🟢 Official Homepage: http://adni.loni.usc.edu/
🟢 Number of articles that used this dataset: 28
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
📝 Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
📝 Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge
📝 Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
📝 An automated machine learning framework to optimize radiomics model construction validated on twelve clinical applications
📝 AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans
📝 The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
📝 TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data
📝 Alzheimer's Disease Brain MRI Classification: Challenges and Insights
📝 Inference of nonlinear causal effects with GWAS summary data
==================================
🔴 For more datasets resources:
✓ https://t.me/DataScienceT
❤6
🟢 Name Of Dataset: MegaDepth
🟢 Description Of Dataset:
The MegaDepth dataset is a dataset for single-view depth prediction that includes 196 different locations reconstructed from COLMAP SfM/MVS.Source:MegaDepth: Learning Single-View Depth Prediction from Internet Photos
🟢 Official Homepage: http://www.cs.cornell.edu/projects/megadepth/
🟢 Number of articles that used this dataset: 150
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
📝 Depth Anything V2
📝 Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
📝 LightGlue: Local Feature Matching at Light Speed
📝 LoFTR: Detector-Free Local Feature Matching with Transformers
📝 3D Ken Burns Effect from a Single Image
📝 Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
📝 Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image
📝 Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction
📝 MegaDepth: Learning Single-View Depth Prediction from Internet Photos
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
The MegaDepth dataset is a dataset for single-view depth prediction that includes 196 different locations reconstructed from COLMAP SfM/MVS.Source:MegaDepth: Learning Single-View Depth Prediction from Internet Photos
🟢 Official Homepage: http://www.cs.cornell.edu/projects/megadepth/
🟢 Number of articles that used this dataset: 150
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
📝 Depth Anything V2
📝 Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
📝 LightGlue: Local Feature Matching at Light Speed
📝 LoFTR: Detector-Free Local Feature Matching with Transformers
📝 3D Ken Burns Effect from a Single Image
📝 Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
📝 Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image
📝 Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction
📝 MegaDepth: Learning Single-View Depth Prediction from Internet Photos
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤8
🟢 Name Of Dataset: CelebA-HQ
🟢 Description Of Dataset:
TheCelebA-HQdataset is a high-quality version of CelebA that consists of 30,000 images at 1024×1024 resolution.Source:IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
🟢 Official Homepage: https://github.com/tkarras/progressive_growing_of_gans
🟢 Number of articles that used this dataset: 946
🟢 Dataset Loaders:
tkarras/progressive_growing_of_gans:
https://github.com/tkarras/progressive_growing_of_gans
tensorflow/datasets:
https://www.tensorflow.org/datasets/catalog/celeb_a_hq
🟢 Articles related to the dataset:
📝 High-Resolution Image Synthesis with Latent Diffusion Models
📝 DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
📝 Towards Real-World Blind Face Restoration with Generative Facial Prior
📝 Towards Robust Blind Face Restoration with Codebook Lookup Transformer
📝 A Style-Based Generator Architecture for Generative Adversarial Networks
📝 Vector-quantized Image Modeling with Improved VQGAN
📝 Resolution-robust Large Mask Inpainting with Fourier Convolutions
📝 GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond
📝 Texture Memory-Augmented Deep Patch-Based Image Inpainting
📝 High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
TheCelebA-HQdataset is a high-quality version of CelebA that consists of 30,000 images at 1024×1024 resolution.Source:IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
🟢 Official Homepage: https://github.com/tkarras/progressive_growing_of_gans
🟢 Number of articles that used this dataset: 946
🟢 Dataset Loaders:
tkarras/progressive_growing_of_gans:
https://github.com/tkarras/progressive_growing_of_gans
tensorflow/datasets:
https://www.tensorflow.org/datasets/catalog/celeb_a_hq
🟢 Articles related to the dataset:
📝 High-Resolution Image Synthesis with Latent Diffusion Models
📝 DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
📝 Towards Real-World Blind Face Restoration with Generative Facial Prior
📝 Towards Robust Blind Face Restoration with Codebook Lookup Transformer
📝 A Style-Based Generator Architecture for Generative Adversarial Networks
📝 Vector-quantized Image Modeling with Improved VQGAN
📝 Resolution-robust Large Mask Inpainting with Fourier Convolutions
📝 GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond
📝 Texture Memory-Augmented Deep Patch-Based Image Inpainting
📝 High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤4
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❤2
🟢 Name Of Dataset: BlendedMVS
🟢 Description Of Dataset:
BlendedMVSis a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. The dataset was created by applying a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, these mesh models were rendered to color images and depth maps.Source:BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
🟢 Official Homepage: https://github.com/YoYo000/BlendedMVS
🟢 Number of articles that used this dataset: 104
🟢 Dataset Loaders:
YoYo000/BlendedMVS:
https://github.com/YoYo000/BlendedMVS
🟢 Articles related to the dataset:
📝 Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
📝 Depth Anything V2
📝 NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
📝 Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
📝 Volume Rendering of Neural Implicit Surfaces
📝 Neural Sparse Voxel Fields
📝 BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
📝 Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction
📝 SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views
📝 Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
BlendedMVSis a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. The dataset was created by applying a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, these mesh models were rendered to color images and depth maps.Source:BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
🟢 Official Homepage: https://github.com/YoYo000/BlendedMVS
🟢 Number of articles that used this dataset: 104
🟢 Dataset Loaders:
YoYo000/BlendedMVS:
https://github.com/YoYo000/BlendedMVS
🟢 Articles related to the dataset:
📝 Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
📝 Depth Anything V2
📝 NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
📝 Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
📝 Volume Rendering of Neural Implicit Surfaces
📝 Neural Sparse Voxel Fields
📝 BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
📝 Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction
📝 SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views
📝 Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤6
🟢 Name Of Dataset: EPIC-KITCHENS-100
🟢 Description Of Dataset:
This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (EPIC-KITCHENS-55), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection also enables evaluating the "test of time" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected under the same hypotheses albeit "two years on". The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.
🟢 Official Homepage: https://epic-kitchens.github.io/2021
🟢 Number of articles that used this dataset: 160
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 MoViNets: Mobile Video Networks for Efficient Video Recognition
📝 Domain-Adversarial Training of Neural Networks
📝 BMN: Boundary-Matching Network for Temporal Action Proposal Generation
📝 Adversarial Discriminative Domain Adaptation
📝 Attention Bottlenecks for Multimodal Fusion
📝 Audiovisual Masked Autoencoders
📝 Multiview Transformers for Video Recognition
📝 ViViT: A Video Vision Transformer
📝 Magma: A Foundation Model for Multimodal AI Agents
📝 V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (EPIC-KITCHENS-55), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection also enables evaluating the "test of time" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected under the same hypotheses albeit "two years on". The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.
🟢 Official Homepage: https://epic-kitchens.github.io/2021
🟢 Number of articles that used this dataset: 160
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 MoViNets: Mobile Video Networks for Efficient Video Recognition
📝 Domain-Adversarial Training of Neural Networks
📝 BMN: Boundary-Matching Network for Temporal Action Proposal Generation
📝 Adversarial Discriminative Domain Adaptation
📝 Attention Bottlenecks for Multimodal Fusion
📝 Audiovisual Masked Autoencoders
📝 Multiview Transformers for Video Recognition
📝 ViViT: A Video Vision Transformer
📝 Magma: A Foundation Model for Multimodal AI Agents
📝 V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤3
🟢 Name Of Dataset: CARLA (Car Learning to Act)
🟢 Description Of Dataset:
CARLA(CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4. Technically, it operates similarly to, as an open source layer over Unreal Engine 4 that provides sensors in the form of RGB cameras (with customizable positions), ground truth depth maps, ground truth semantic segmentation maps with 12 semantic classes designed for driving (road, lane marking, traffic sign, sidewalk and so on), bounding boxes for dynamic objects in the environment, and measurements of the agent itself (vehicle location and orientation).Source:Synthetic Data for Deep Learning
🟢 Official Homepage: https://carla.org/
🟢 Number of articles that used this dataset: 1316
🟢 Dataset Loaders:
joedlopes/carla-simulator-multimodal-sensing:
https://github.com/joedlopes/carla-simulator-multimodal-sensing
🟢 Articles related to the dataset:
📝 Synthetic Dataset Generation for Adversarial Machine Learning Research
📝 End-to-end Autonomous Driving: Challenges and Frontiers
📝 OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
📝 On the Practicality of Deterministic Epistemic Uncertainty
📝 D4RL: Datasets for Deep Data-Driven Reinforcement Learning
📝 Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)
📝 Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
📝 Label Efficient Visual Abstractions for Autonomous Driving
📝 Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
📝 TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
CARLA(CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4. Technically, it operates similarly to, as an open source layer over Unreal Engine 4 that provides sensors in the form of RGB cameras (with customizable positions), ground truth depth maps, ground truth semantic segmentation maps with 12 semantic classes designed for driving (road, lane marking, traffic sign, sidewalk and so on), bounding boxes for dynamic objects in the environment, and measurements of the agent itself (vehicle location and orientation).Source:Synthetic Data for Deep Learning
🟢 Official Homepage: https://carla.org/
🟢 Number of articles that used this dataset: 1316
🟢 Dataset Loaders:
joedlopes/carla-simulator-multimodal-sensing:
https://github.com/joedlopes/carla-simulator-multimodal-sensing
🟢 Articles related to the dataset:
📝 Synthetic Dataset Generation for Adversarial Machine Learning Research
📝 End-to-end Autonomous Driving: Challenges and Frontiers
📝 OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
📝 On the Practicality of Deterministic Epistemic Uncertainty
📝 D4RL: Datasets for Deep Data-Driven Reinforcement Learning
📝 Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)
📝 Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
📝 Label Efficient Visual Abstractions for Autonomous Driving
📝 Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
📝 TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
👍1
🟢 Name Of Dataset: Speech Commands
🟢 Description Of Dataset:
Speech Commandsis an audio dataset of spoken words designed to help train and evaluate keyword spotting systems .
🟢 Official Homepage: https://arxiv.org/abs/1804.03209
🟢 Number of articles that used this dataset: 384
🟢 Dataset Loaders:
activeloopai/Hub:
https://docs.activeloop.ai/datasets/speech-commands-dataset
tensorflow/datasets:
https://www.tensorflow.org/datasets/catalog/speech_commands
pytorch/audio:
https://pytorch.org/audio/stable/datasets.html#torchaudio.datasets.SPEECHCOMMANDS
tk-rusch/lem:
https://github.com/tk-rusch/lem
🟢 Articles related to the dataset:
📝 Towards Learning a Universal Non-Semantic Representation of Speech
📝 Streaming keyword spotting on mobile devices
📝 MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition
📝 Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers
📝 ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
📝 Efficiently Modeling Long Sequences with Structured State Spaces
📝 Diagonal State Spaces are as Effective as Structured State Spaces
📝 Meta-Transformer: A Unified Framework for Multimodal Learning
📝 AST: Audio Spectrogram Transformer
📝 Training Keyword Spotters with Limited and Synthesized Speech Data
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
Speech Commandsis an audio dataset of spoken words designed to help train and evaluate keyword spotting systems .
🟢 Official Homepage: https://arxiv.org/abs/1804.03209
🟢 Number of articles that used this dataset: 384
🟢 Dataset Loaders:
activeloopai/Hub:
https://docs.activeloop.ai/datasets/speech-commands-dataset
tensorflow/datasets:
https://www.tensorflow.org/datasets/catalog/speech_commands
pytorch/audio:
https://pytorch.org/audio/stable/datasets.html#torchaudio.datasets.SPEECHCOMMANDS
tk-rusch/lem:
https://github.com/tk-rusch/lem
🟢 Articles related to the dataset:
📝 Towards Learning a Universal Non-Semantic Representation of Speech
📝 Streaming keyword spotting on mobile devices
📝 MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition
📝 Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers
📝 ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
📝 Efficiently Modeling Long Sequences with Structured State Spaces
📝 Diagonal State Spaces are as Effective as Structured State Spaces
📝 Meta-Transformer: A Unified Framework for Multimodal Learning
📝 AST: Audio Spectrogram Transformer
📝 Training Keyword Spotters with Limited and Synthesized Speech Data
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤2
🟢 Name Of Dataset: TUM RGB-D
🟢 Description Of Dataset:
TUM RGB-Dis an RGB-D dataset. It contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz).Source:https://vision.in.tum.de/data/datasets/rgbd-dataset
🟢 Official Homepage: https://vision.in.tum.de/data/datasets/rgbd-dataset
🟢 Number of articles that used this dataset: 234
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
📝 pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM
📝 DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
📝 Gaussian Splatting SLAM
📝 ORB-SLAM: a Versatile and Accurate Monocular SLAM System
📝 NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
📝 How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
📝 Robust Keyframe-based Dense SLAM with an RGB-D Camera
📝 DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
📝 Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
TUM RGB-Dis an RGB-D dataset. It contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz).Source:https://vision.in.tum.de/data/datasets/rgbd-dataset
🟢 Official Homepage: https://vision.in.tum.de/data/datasets/rgbd-dataset
🟢 Number of articles that used this dataset: 234
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
📝 pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM
📝 DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
📝 Gaussian Splatting SLAM
📝 ORB-SLAM: a Versatile and Accurate Monocular SLAM System
📝 NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
📝 How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
📝 Robust Keyframe-based Dense SLAM with an RGB-D Camera
📝 DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
📝 Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤4🔥1
🟢 Name Of Dataset: ITDD (Industrial Textile Defect Detection)
🟢 Description Of Dataset:
The Industrial Textile Defect Detection (ITDD) dataset includes 1885 industrial textile images categorized into 4 categories: cotton fabric, dyed fabric, hemp fabric, and plaid fabric. These classes are collected from the industrial production sites of WEIQIAO Textile. ITDD is an upgraded version of WFDD that reorganizes three original classes and adds one new class.
🟢 Official Homepage: https://github.com/cqylunlun/CRAS?tab=readme-ov-file#dataset-release
🟢 Number of articles that used this dataset: 1
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
The Industrial Textile Defect Detection (ITDD) dataset includes 1885 industrial textile images categorized into 4 categories: cotton fabric, dyed fabric, hemp fabric, and plaid fabric. These classes are collected from the industrial production sites of WEIQIAO Textile. ITDD is an upgraded version of WFDD that reorganizes three original classes and adds one new class.
🟢 Official Homepage: https://github.com/cqylunlun/CRAS?tab=readme-ov-file#dataset-release
🟢 Number of articles that used this dataset: 1
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤3
🟢 Name Of Dataset: CAMELS Multifield Dataset
🟢 Description Of Dataset:
CMD is a publicly available collection of hundreds of thousands 2D maps and 3D grids containing different properties of the gas, dark matter, and stars from more than 2,000 different universes. The data has been generated from thousands of state-of-the-art (magneto-)hydrodynamic and gravity-only N-body simulations from the CAMELS project.Each 2D map and 3D grid has a set of labels associated to it: 2 cosmological parameters characterizing fundamental properties of the Universe, and 4 astrophysical parameters parametrizing the strength of astrophysical processes such as feedback from supernova and supermassive black-holes.The main task this dataset was designed is to perform a robust inference on the value of the cosmological parameters from each map and grid. The data itself was generated from two completely different set of simulations, and it is not obvious that training one model on one will work when predicting on the other. Since simulations of the real Universe may never be perfect, this dataset provides the data to tackle this problem.Solving this problem will help cosmologists to constrain the value of the cosmological parameters with the highest accuracy and therefore unveil the mysteries of our Universe. CMD can also be used for many other tasks, such as field mapping and super-resolution.
🟢 Official Homepage: https://camels-multifield-dataset.readthedocs.io
🟢 Number of articles that used this dataset: 6
🟢 Dataset Loaders:
franciscovillaescusa/CMD:
https://camels-multifield-dataset.readthedocs.io
🟢 Articles related to the dataset:
📝 The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
📝 The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
📝 Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter
📝 Multifield Cosmology with Artificial Intelligence
📝 Robust marginalization of baryonic effects for cosmological inference at the field level
📝 Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
CMD is a publicly available collection of hundreds of thousands 2D maps and 3D grids containing different properties of the gas, dark matter, and stars from more than 2,000 different universes. The data has been generated from thousands of state-of-the-art (magneto-)hydrodynamic and gravity-only N-body simulations from the CAMELS project.Each 2D map and 3D grid has a set of labels associated to it: 2 cosmological parameters characterizing fundamental properties of the Universe, and 4 astrophysical parameters parametrizing the strength of astrophysical processes such as feedback from supernova and supermassive black-holes.The main task this dataset was designed is to perform a robust inference on the value of the cosmological parameters from each map and grid. The data itself was generated from two completely different set of simulations, and it is not obvious that training one model on one will work when predicting on the other. Since simulations of the real Universe may never be perfect, this dataset provides the data to tackle this problem.Solving this problem will help cosmologists to constrain the value of the cosmological parameters with the highest accuracy and therefore unveil the mysteries of our Universe. CMD can also be used for many other tasks, such as field mapping and super-resolution.
🟢 Official Homepage: https://camels-multifield-dataset.readthedocs.io
🟢 Number of articles that used this dataset: 6
🟢 Dataset Loaders:
franciscovillaescusa/CMD:
https://camels-multifield-dataset.readthedocs.io
🟢 Articles related to the dataset:
📝 The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
📝 The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
📝 Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter
📝 Multifield Cosmology with Artificial Intelligence
📝 Robust marginalization of baryonic effects for cosmological inference at the field level
📝 Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤4👍2
🟢 Name Of Dataset: ETT (Electricity Transformer Temperature)
🟢 Description Of Dataset:
TheElectricity Transformer Temperature(ETT) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months.Source:https://arxiv.org/pdf/2012.07436.pdf
🟢 Official Homepage: https://github.com/zhouhaoyi/ETDataset
🟢 Number of articles that used this dataset: 318
🟢 Dataset Loaders:
zhouhaoyi/ETDataset:
https://github.com/zhouhaoyi/ETDataset
🟢 Articles related to the dataset:
📝 TSMixer: An All-MLP Architecture for Time Series Forecasting
📝 A decoder-only foundation model for time-series forecasting
📝 Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting
📝 Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
📝 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
📝 A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
📝 iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
📝 TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis
📝 TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
📝 FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
TheElectricity Transformer Temperature(ETT) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months.Source:https://arxiv.org/pdf/2012.07436.pdf
🟢 Official Homepage: https://github.com/zhouhaoyi/ETDataset
🟢 Number of articles that used this dataset: 318
🟢 Dataset Loaders:
zhouhaoyi/ETDataset:
https://github.com/zhouhaoyi/ETDataset
🟢 Articles related to the dataset:
📝 TSMixer: An All-MLP Architecture for Time Series Forecasting
📝 A decoder-only foundation model for time-series forecasting
📝 Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting
📝 Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
📝 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
📝 A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
📝 iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
📝 TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis
📝 TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
📝 FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤6
🟢 Name Of Dataset: OoDIS (Anomaly Instance Segmentation Benchmark)
🟢 Description Of Dataset:
OoDIS is a benchmark dataset for anomaly instance segmentation, crucial for autonomous vehicle safety. It extends existing anomaly segmentation benchmarks to focus on the segmentation of individual out-of-distribution (OOD) objects.The dataset addresses the need for identifying and segmenting unknown objects, which are critical to avoid accidents. It includes diverse scenes with various anomalies, pushing the boundaries of current segmentation capabilities.The benchmark is focused on evaluation of detection and instance segmentation of unexpected obstacles on roads.For more details, refer to theOoDIS paper
🟢 Official Homepage: https://kumuji.github.io/oodis_website/
🟢 Number of articles that used this dataset: 5
🟢 Dataset Loaders:
kumuji/ugains:
https://github.com/kumuji/ugains
🟢 Articles related to the dataset:
📝 Unmasking Anomalies in Road-Scene Segmentation
📝 UGainS: Uncertainty Guided Anomaly Instance Segmentation
📝 OoDIS: Anomaly Instance Segmentation Benchmark
📝 Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
📝 On the Potential of Open-Vocabulary Models for Object Detection in Unusual Street Scenes
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
OoDIS is a benchmark dataset for anomaly instance segmentation, crucial for autonomous vehicle safety. It extends existing anomaly segmentation benchmarks to focus on the segmentation of individual out-of-distribution (OOD) objects.The dataset addresses the need for identifying and segmenting unknown objects, which are critical to avoid accidents. It includes diverse scenes with various anomalies, pushing the boundaries of current segmentation capabilities.The benchmark is focused on evaluation of detection and instance segmentation of unexpected obstacles on roads.For more details, refer to theOoDIS paper
🟢 Official Homepage: https://kumuji.github.io/oodis_website/
🟢 Number of articles that used this dataset: 5
🟢 Dataset Loaders:
kumuji/ugains:
https://github.com/kumuji/ugains
🟢 Articles related to the dataset:
📝 Unmasking Anomalies in Road-Scene Segmentation
📝 UGainS: Uncertainty Guided Anomaly Instance Segmentation
📝 OoDIS: Anomaly Instance Segmentation Benchmark
📝 Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
📝 On the Potential of Open-Vocabulary Models for Object Detection in Unusual Street Scenes
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤4
🟢 Name Of Dataset: InfoSeek (Visual Information Seeking)
🟢 Description Of Dataset:
In this project, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge. Using InfoSeek, we analyze various pre-trained visual question answering models and gain insights into their characteristics. Our findings reveal that state-of-the-art pre-trained multi-modal models (e.g., PaLI-X, BLIP2, etc.) face challenges in answering visual information-seeking questions, but fine-tuning on the InfoSeek dataset elicits models to use fine-grained knowledge that was learned during their pre-training.
🟢 Official Homepage: https://open-vision-language.github.io/infoseek/
🟢 Number of articles that used this dataset: 35
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
📝 LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
📝 Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent
📝 Ming-Omni: A Unified Multimodal Model for Perception and Generation
📝 Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering
📝 PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
📝 Safety of Multimodal Large Language Models on Images and Texts
📝 PaLI-X: On Scaling up a Multilingual Vision and Language Model
📝 MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
📝 Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
In this project, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge. Using InfoSeek, we analyze various pre-trained visual question answering models and gain insights into their characteristics. Our findings reveal that state-of-the-art pre-trained multi-modal models (e.g., PaLI-X, BLIP2, etc.) face challenges in answering visual information-seeking questions, but fine-tuning on the InfoSeek dataset elicits models to use fine-grained knowledge that was learned during their pre-training.
🟢 Official Homepage: https://open-vision-language.github.io/infoseek/
🟢 Number of articles that used this dataset: 35
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
📝 LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
📝 Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent
📝 Ming-Omni: A Unified Multimodal Model for Perception and Generation
📝 Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering
📝 PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
📝 Safety of Multimodal Large Language Models on Images and Texts
📝 PaLI-X: On Scaling up a Multilingual Vision and Language Model
📝 MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
📝 Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤4
🟢 Name Of Dataset: UIIS10K (General Underwater Image Instance Segmentation dataset 10K)
🟢 Description Of Dataset:
We propose a large-scale underwater instance segmentation dataset, UIIS10K, which includes 10,048 images with pixel-level annotations for 10 categories. As far as we know, this is the largest underwater instance segmentation dataset available and can be used as a benchmark for evaluating underwater segmentation methods.
🟢 Official Homepage: https://github.com/LiamLian0727/UIIS10K
🟢 Number of articles that used this dataset: 3
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 WaterMask: Instance Segmentation for Underwater Imagery
📝 A Unified Image-Dense Annotation Generation Model for Underwater Scenes
📝 UWSAM: Segment Anything Model Guided Underwater Instance Segmentation and A Large-scale Benchmark Dataset
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
We propose a large-scale underwater instance segmentation dataset, UIIS10K, which includes 10,048 images with pixel-level annotations for 10 categories. As far as we know, this is the largest underwater instance segmentation dataset available and can be used as a benchmark for evaluating underwater segmentation methods.
🟢 Official Homepage: https://github.com/LiamLian0727/UIIS10K
🟢 Number of articles that used this dataset: 3
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 WaterMask: Instance Segmentation for Underwater Imagery
📝 A Unified Image-Dense Annotation Generation Model for Underwater Scenes
📝 UWSAM: Segment Anything Model Guided Underwater Instance Segmentation and A Large-scale Benchmark Dataset
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤6
🟢 Name Of Dataset: 1
🟢 Description Of Dataset:
111
🟢 Official Homepage: Not found
🟢 Number of articles that used this dataset: 28
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 NeMo Inverse Text Normalization: From Development To Production
📝 Open Deep Search: Democratizing Search with Open-source Reasoning Agents
📝 Deep Learning in Single-Cell Analysis
📝 Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding
📝 UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
📝 Representation Learning with Large Language Models for Recommendation
📝 Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
📝 K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
📝 Semi-supervised Sequence Modeling for Elastic Impedance Inversion
📝 CholecTrack20: A Dataset for Multi-Class Multiple Tool Tracking in Laparoscopic Surgery
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
111
🟢 Official Homepage: Not found
🟢 Number of articles that used this dataset: 28
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 NeMo Inverse Text Normalization: From Development To Production
📝 Open Deep Search: Democratizing Search with Open-source Reasoning Agents
📝 Deep Learning in Single-Cell Analysis
📝 Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding
📝 UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
📝 Representation Learning with Large Language Models for Recommendation
📝 Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
📝 K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
📝 Semi-supervised Sequence Modeling for Elastic Impedance Inversion
📝 CholecTrack20: A Dataset for Multi-Class Multiple Tool Tracking in Laparoscopic Surgery
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
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