SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Mathilde Caron)

09.14.2020 - By Machine Learning Street Talk (MLST)

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This week Dr. Tim Scarfe, Yannic Lightspeed Kicher, Sayak Paul and Ayush Takur interview Mathilde Caron from Facebook Research (FAIR).
We discuss Mathilde's paper which she wrote with her collaborators "SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments" @ https://arxiv.org/pdf/2006.09882.pdf 
This paper is the latest unsupervised contrastive visual representations algorithm and has a new data augmentation strategy and also a new online clustering strategy. 
Note; Other authors; Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin
Sayak Paul -  @RisingSayak / https://www.linkedin.com/in/sayak-paul/
Ayush Thakur - @ayushthakur0
 / https://www.linkedin.com/in/ayush-thakur-731914149/
The article they wrote;

00:00:00 Yannic probability challenge (CAN YOU SOLVE IT?)
00:01:29 Intro topic (Tim)
00:08:18 Yannic take
00:09:33 Intro show and guests
00:11:29 SWaV elevator pitch 
00:17:31 Clustering approach in general
00:21:17 Sayak and Ayush's article on SWaV 
00:23:49 Optional transport problem / Sinkhorn-Knopp algorithm
00:31:43 Is clustering a natural approach for this?
00:44:19 Image augmentations 
00:46:20 Priors vs experience (data)
00:48:32 Life at FAIR 
00:52:33 Progress of image augmentation 
00:56:10 When things do not go to plan with research
01:01:04 Question on architecture
01:01:43 SWaV Results
01:06:26 Reproducing Matilde's code
01:14:51 Do we need the whole dataset to set clustering loss
01:16:40 Self-supervised learning and transfer learning
01:23:25 Link to attention mechanism)
01:24:41 Sayak final thought why unsupervised better
01:25:56 Outro

"Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or “views”) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a “swapped” prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks."

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