Summary of Paper - Representation Learning Via Invariant Causal Mechanisms (ReLIC)
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Summary of Paper - Representation Learning Via Invariant Causal Mechanisms (ReLIC)

TL;DR - Here is the the summary presentation & the annotated paper.

Recently I started reading about latest progress in self-supervised learning (SSL) 😃. I was particularly more interested towards that area due to limitations in supervised learning and the potential scalability that self-supervised learning could offer.

So, during the past couple of weeks I studied several papers in this area which are underlined in red below. Particularly the SimCLR & ReLIC papers and also a review paper on contrastive learning. Then I came up with a presentation as an introduction to self-supervised learning while summarizing the contributions of the ReLIC paper as well. I presented this during one of our weekly journal club meetings 😃. With the suggestions & feedback I got from my peers, I got motivated to write & discuss more about the papers I read.

(Selecting serveral papers to read)

If you want to get a quick introduction to SSL and get to some details about the ReLIC paper, please check out the presentation slides below or click here to open it a new tab.

Annotated Paper

 
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