Do Better ImageNet Models Transfer Better?
By Simon Kornblith, Jonathon Shlens, Quoc V. Le: https://arxiv.org/abs/1805.08974
#ComputerVision #PatternRecognition #MachineLearning
By Simon Kornblith, Jonathon Shlens, Quoc V. Le: https://arxiv.org/abs/1805.08974
#ComputerVision #PatternRecognition #MachineLearning
SlowFast Networks for Video Recognition
Feichtenhofer et al.: https://arxiv.org/abs/1812.03982
#MachineLearning #ComputerVision #DeepLearning #PatternRecognition #Technology
Feichtenhofer et al.: https://arxiv.org/abs/1812.03982
#MachineLearning #ComputerVision #DeepLearning #PatternRecognition #Technology
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SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
Jo and Park: https://arxiv.org/abs/1902.06838f
GitHub: https://github.com/JoYoungjoo/SC-FEGAN
#ComputerVision #GenerativeAdversarialNetwork #PatternRecognition
Jo and Park: https://arxiv.org/abs/1902.06838f
GitHub: https://github.com/JoYoungjoo/SC-FEGAN
#ComputerVision #GenerativeAdversarialNetwork #PatternRecognition
Reversible Adversarial Examples
Liu et al.: https://arxiv.org/abs/1811.00189
#ComputerVision #PatternRecognition #MachineLearning #MontréalAI #NeuralNetworks
Liu et al.: https://arxiv.org/abs/1811.00189
#ComputerVision #PatternRecognition #MachineLearning #MontréalAI #NeuralNetworks
Machine learning and artificial intelligence in the quantum domain"
By Vedran Dunjko, Hans J. Briegel: https://arxiv.org/abs/1709.02779
#QuantumPhysics #ArtificialIntelligence #ComputerVision #MontrealAI #PatternRecognition
By Vedran Dunjko, Hans J. Briegel: https://arxiv.org/abs/1709.02779
#QuantumPhysics #ArtificialIntelligence #ComputerVision #MontrealAI #PatternRecognition
This one is a must read - the latest #ComputerVision #PatternRecognition https://deepai.org/publication/relational-action-forecasting
Meta-Sim: Learning to Generate Synthetic Datasets
Kar et al.: https://arxiv.org/abs/1904.11621 @ArtificialIntelligenceArticles
#ComputerVision #PatternRecognition #ArtificialIntelligence
Kar et al.: https://arxiv.org/abs/1904.11621 @ArtificialIntelligenceArticles
#ComputerVision #PatternRecognition #ArtificialIntelligence
Fast AutoAugment
Lim et al.:
https://arxiv.org/abs/1905.00397
Code:
https://github.com/KakaoBrain/fast-autoaugment
#MachineLearning #ComputerVision #PatternRecognition
Lim et al.:
https://arxiv.org/abs/1905.00397
Code:
https://github.com/KakaoBrain/fast-autoaugment
#MachineLearning #ComputerVision #PatternRecognition
arXiv.org
Fast AutoAugment
Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment has been proposed as an algorithm to automatically search for...
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
arXiv.org
Visualizing the Consequences of Climate Change Using...
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our...
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
arXiv.org
Visualizing the Consequences of Climate Change Using...
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our...
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
A key conference quality indicator is low paper acceptance rates. The CVPR 2019 paper acceptance rate dropped to 25.1 percent from last year’s 29.6 percent 🤓☝️
The list of all 1300 research papers accepted for CVPR 2019 is available here: http://openaccess.thecvf.com/CVPR2019.py
Given you spend 1 hour to read 1 article and the rate of 8 articles per day, it will take you about 6 months to read all of them. You'd better start right now 🙃
#CVPR2019 #computervision #patternrecognition #deeplearning #machinelearning
The list of all 1300 research papers accepted for CVPR 2019 is available here: http://openaccess.thecvf.com/CVPR2019.py
Given you spend 1 hour to read 1 article and the rate of 8 articles per day, it will take you about 6 months to read all of them. You'd better start right now 🙃
#CVPR2019 #computervision #patternrecognition #deeplearning #machinelearning
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Simon et al.: https://arxiv.org/abs/1803.06199
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Simon et al.: https://arxiv.org/abs/1803.06199
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
Learning Data Augmentation Strategies for Object Detection
Zoph et al.: https://arxiv.org/abs/1906.11172
#ArtificialIntelligence #ComputerVision #PatternRecognition
Zoph et al.: https://arxiv.org/abs/1906.11172
#ArtificialIntelligence #ComputerVision #PatternRecognition
arXiv.org
Learning Data Augmentation Strategies for Object Detection
Data augmentation is a critical component of training deep learning models. Although data augmentation has been shown to significantly improve image classification, its potential has not been...
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
Natural Adversarial Examples
Hendrycks et al.: https://arxiv.org/abs/1907.07174) arxiv.org/abs/1907.07174
Dataset and code: https://github.com/hendrycks/natural-adv-examples
#MachineLearning #ComputerVision #PatternRecognition
Hendrycks et al.: https://arxiv.org/abs/1907.07174) arxiv.org/abs/1907.07174
Dataset and code: https://github.com/hendrycks/natural-adv-examples
#MachineLearning #ComputerVision #PatternRecognition
arXiv.org
Natural Adversarial Examples
We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to...
Check the final ICCV'19 program here: http://iccv2019.thecvf.com/
It has been a pleasure (and a lot of work) to serve as program chair along with Pr. Svetlana Lazebnik, Pr. Ming-Hsuan Yang and Pr. In So Kweon for the
IEEE/CVF International Conference in #computervision (ICCV'19)
- 4350 full submissions (twice the number of ICCV'17)
- 175 ACs, 1,500 reviewers,
- 13,000 reviews
4 award papers (11 nominations), 200 orals, 850 posters, 25 complaints.
See you in Seoul.
#computervision, #patternrecognition, #artificialintelligence, #machinelearning, #deeplearning
It has been a pleasure (and a lot of work) to serve as program chair along with Pr. Svetlana Lazebnik, Pr. Ming-Hsuan Yang and Pr. In So Kweon for the
IEEE/CVF International Conference in #computervision (ICCV'19)
- 4350 full submissions (twice the number of ICCV'17)
- 175 ACs, 1,500 reviewers,
- 13,000 reviews
4 award papers (11 nominations), 200 orals, 850 posters, 25 complaints.
See you in Seoul.
#computervision, #patternrecognition, #artificialintelligence, #machinelearning, #deeplearning
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Simon et al.: https://arxiv.org/abs/1803.06199
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Simon et al.: https://arxiv.org/abs/1803.06199
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 1
Blog by Ayoosh Kathuria: https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Blog by Ayoosh Kathuria: https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition
Paperspace by DigitalOcean Blog
Tutorial on implementing YOLO v3 from scratch in PyTorch
Tutorial on building YOLO v3 detector from scratch detailing how to create the network architecture from a configuration file, load the weights and designing input/output pipelines.