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

TL;DR - Here is the the summaryhttps://papers.ramith.fyi/Representation%20Learning%20Via%20Invariant%20Causal%20Mechanisms/Representation%20Learning%20Via%20Invariant%20Causal%20Mechanisms - PPT.pdf［presentation］ & thehttps://papers.ramith.fyi/Representation%20Learning%20Via%20Invariant%20Causal%20Mechanisms/Representation%20Learning%20Via%20Invariant%20Causal%20Mechanisms.pdf［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.

Figure 1: (Some slides of my presentation embeded below)

Figure 2: (Some slides of my presentation embeded below)

Figure 3: (Some slides of my presentation embeded below)

Figure 4: (Some slides of my presentation embeded below)

Figure 5: (Some slides of my presentation embeded below)

So, during the past couple of weeks I studied several papers in this area which are underlined in red below. Particularly the SimCLR[1]& ReLIC[1]papers and also areview paper on contrastive learning. Then I came up with apresentation as an introduction to self-supervised learning while summarizing the contributions of the ReLIC paper as well. I presented this during one of ourweekly journal club meetings 😃. With the suggestions & feedback I got from my peers, I got motivated to write & discuss more about the papers I read.

Figure 6: (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 orclick here to open it a new tab. This is an embeddedMicrosoft Office presentation, powered byOffice . https://papers.ramith.fyi/Representation%20Learning%20Via%20Invariant%20Causal%20Mechanisms/Representation%20Learning%20Via%20Invariant%20Causal%20Mechanisms.pdf［Annotated Paper］