Скачать или смотреть MedAI Session 8: Self-supervision & Contrastive Frameworks: a vision-based review | Nandita Bhaskhar

Title: Self-supervision & Contrastive Frameworks: a vision-based review

Speaker: Nandita Bhaskhar

Abstract:
Self-supervised representation learning and contrastive techniques have picked up a lot interest in the last couple of years, especially in computer vision. Until recently, deep learning's successes thus far have been associated with a supervised learning paradigm, wherein labelled datasets are used to train models on specific tasks. This need for labelled datasets has been identified as the bottleneck for scaling deep learning models across various tasks and domains. They rely heavily on costly, time-consuming dataset curation and labelling schemes.

Self-supervision allows us to learn representations from large unlabelled datasets. Instead of relying on labels for inputs, it depends on designing suitable pre-text tasks to generate pseudo-labels from the data directly. Contrastive learning refers to a special subset of these self-supervised methods that have achieved the most success recently. In this talk, I will go over the top 6 recent frameworks - SimCLR, MoCo V2, BYOL, SwAV, DINO and Barlow Twins, giving a deeper dive into their methodology & performance and comparing each of the frameworks' strengths and weaknesses and discuss their suitability for applications in the medical domain.

Speaker Bio:
Nandita Bhaskhar (www.stanford.edu/~nanbhas) is a PhD student in the Department of Electrical Engineering at Stanford University advised by Daniel Rubin. She received her B.Tech in Electronics Engineering from the Indian Institute of Information Technology, IIIT, with the highest honours. She is broadly interested in developing machine learning methodology for medical applications. Her current research focuses on observational supervision and self-supervision for leveraging unlabelled medical data and out-of-distribution detection for reliable clinical deployment. Outside of research, her curiosity lies in a wide gamut of things including but not restricted to biking, social dance, travelling, creative writing, music, getting lost, hiking and exploring new things.

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Organized by members of the Rubin Lab (http://rubinlab.stanford.edu​​)
Nandita Bhaskhar (https://www.stanford.edu/~nanbhas​​)
Siyi Tang (https://siyitang.me​​)