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

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;

https://app.wandb.ai/authors/swav-tf/reports/Unsupervised-Visual-Representation-Learning-with-SwAV--VmlldzoyMjg3Mzg

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

Abstract; 

"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."