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[D] Patterns of Self-Supervised Learning

I wrote up a short article introducing self-supervised learning and noting down common recurring patterns that I’ve observed in several self-supervised problem setups. Feedback welcome. In general, one has to be quite creative setting up the right ‘input’ and ‘output’ for learning a particular object’s representation.

Are there other common patterns that others have observed?

How do we compare the representations learned from two different self-supervised setups for the same object type, e.g., rotation vs patch-based, BERT-like masked loss vs word vectors?

submitted by /u/ekshaks
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.