Dino/Dino v2 explained: Self-distillation with no labels & etc. #FYI #Tips #Explained #Tutorial
1. https://medium.com/@anuj.dutt9/emerging-properties-in-self-supervised-vision-transformers-dino-paper-summary-4c7a6ed68161 Original Dino
2. https://encord.com/blog/dinov2-self-supervised-learning-explained/
3. https://www.picsellia.com/post/dinov2-steps-by-steps-explanations-picsellia
4. https://www.ai-bites.net/dino-v2-learning-robust-visual-features-without-supervision-model-explained/
5. https://blog.marvik.ai/2023/05/16/dinov2-exploring-self-supervised-vision-transformers/
Original papers:
1. https://arxiv.org/abs/2104.14294 Emerging Properties in Self-Supervised Vision Transformers (Dino)
2. https://arxiv.org/abs/2304.07193 DINOv2: Learning Robust Visual Features without Supervision
3. https://arxiv.org/abs/2309.16588 Vision Transformers Need Registers
1. https://medium.com/@anuj.dutt9/emerging-properties-in-self-supervised-vision-transformers-dino-paper-summary-4c7a6ed68161 Original Dino
2. https://encord.com/blog/dinov2-self-supervised-learning-explained/
3. https://www.picsellia.com/post/dinov2-steps-by-steps-explanations-picsellia
4. https://www.ai-bites.net/dino-v2-learning-robust-visual-features-without-supervision-model-explained/
5. https://blog.marvik.ai/2023/05/16/dinov2-exploring-self-supervised-vision-transformers/
Original papers:
1. https://arxiv.org/abs/2104.14294 Emerging Properties in Self-Supervised Vision Transformers (Dino)
2. https://arxiv.org/abs/2304.07193 DINOv2: Learning Robust Visual Features without Supervision
3. https://arxiv.org/abs/2309.16588 Vision Transformers Need Registers
Medium
Emerging Properties in Self-Supervised Vision Transformers (DINO) — Paper Summary
Hi Everyone! Today, we’ll unravel the complexities of an intriguing approach in the realm of self-supervised learning, delving into a groundbreaking paper titled “Emerging Properties in…
https://arxiv.org/html/2405.18886v1 Compressing Large Language Models using Low Rank and Low Precision Decomposition #paper
https://github.com/staghado/vit.cpp Inference Vision Transformer (ViT) in plain C/C++ with ggml
https://github.com/ggerganov/ggml Tensor library for machine learning with Low-level cross-platform implementation
#Frameworks
https://github.com/ggerganov/ggml Tensor library for machine learning with Low-level cross-platform implementation
#Frameworks
GitHub
GitHub - staghado/vit.cpp: Inference Vision Transformer (ViT) in plain C/C++ with ggml
Inference Vision Transformer (ViT) in plain C/C++ with ggml - staghado/vit.cpp
https://arxiv.org/abs/2412.11768
https://github.com/AnonymousAlethiometer/SGD_SaI/
#Paper #Frameworks
https://github.com/AnonymousAlethiometer/SGD_SaI/
#Paper #Frameworks
arXiv.org
No More Adam: Learning Rate Scaling at Initialization is All You Need
In this work, we question the necessity of adaptive gradient methods for training deep neural networks. SGD-SaI is a simple yet effective enhancement to stochastic gradient descent with momentum...
Deep Learning
Dino/Dino v2 explained: Self-distillation with no labels & etc. #FYI #Tips #Explained #Tutorial 1. https://medium.com/@anuj.dutt9/emerging-properties-in-self-supervised-vision-transformers-dino-paper-summary-4c7a6ed68161 Original Dino 2. https://encord.com/blog/dinov2…
https://www.samarkhanna.com/ExPLoRA/ Parameter-Efficient Extended Pre-training to Adapt Vision Transformers under Domain Shifts
#Paper #Framework
#Paper #Framework
Samarkhanna
ExPLoRA
ExPLoRA: Parameter-Efficient Extended Pre-training to Adapt Vision Transformers under Domain Shifts
https://medium.com/version-1/the-rise-of-large-action-models-lams-how-ai-can-understand-and-execute-human-intentions-f59c8e78bc09
Large Action Models
Large Action Models
Medium
The Rise of Large Action Models, LAMs: How AI Can Understand and Execute Human Intentions?
A hot topic and development in the realm artificial intelligence (AI) is Large Action Models, also referred as Large Agentic Models or LAMs…
https://arxiv.org/pdf/2411.07975
JanusFlow: Harmonizing Autoregression and Rectified Flow
for Unified Multimodal Understanding and Generation
#Paper
Finally multimodality on input and output!
JanusFlow: Harmonizing Autoregression and Rectified Flow
for Unified Multimodal Understanding and Generation
#Paper
Finally multimodality on input and output!
https://github.com/trent-b/iterative-stratification scikit-learn compatible cross validators and splitting with stratification for multilabel data. #library
GitHub
GitHub - trent-b/iterative-stratification: scikit-learn cross validators for iterative stratification of multilabel data
scikit-learn cross validators for iterative stratification of multilabel data - trent-b/iterative-stratification
https://paperswithcode.com/methods/category/convolutions
Known types of convolutions, including deformable convolutions and the latest PushPull-Conv
Known types of convolutions, including deformable convolutions and the latest PushPull-Conv
Paperswithcode
Papers with Code - An Overview of Convolutions
Convolutions are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and…
https://huggingface.co/XiaomiMiMo/MiMo-VL-7B-RL
https://github.com/XiaomiMiMo/MiMo-VL
New Vision Language Model(VLM) that outperforms Qwen2.5-VL #models #vlm
https://github.com/XiaomiMiMo/MiMo-VL
New Vision Language Model(VLM) that outperforms Qwen2.5-VL #models #vlm
huggingface.co
XiaomiMiMo/MiMo-VL-7B-RL · Hugging Face
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