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  "text": "Progress in AI is driven by approaches that make weaker assumptions, which allows for better scaling\n\nBut representation learning has relied on strong assumptions like augmentations, masking, cropping, etc... until now!\n\n🎬 Introducing Temporal Difference in Vision (TDV), a new paradigm for representation learning built on a single assumption: causality\n\nTL;DR:\n- We introduce TDV, the first approach to learn good representations without any augmentations, masking, cropping, or pixel-based reconstruction\n- TDV matches SOTA recipes like DINO and iBOT on dense spatial tasks\n- We show that as data scales, weaker assumptions work better\n\n🧵Thread:",
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