@jchencxh Theoretically, we may need the right equivalences and distinctions; practically, yeah, whether those actually get learned also depends on the learning interface/architecture.
@jchencxh Thanks, this helps a lot. Maybe on the supervision side, hierarchy filling needs both: a stable target that anchors varying lower-level realizations, and enough contextual variation to make levels distinguishable.
@jchencxh More generally, do you think that ``filling the hierarchy'' requires varying the relationships among semantic factors across prediction contexts so that different levels become separately identifiable?
@jchencxh Good blog. I really like the hierarchy-filling perspective. Could another way to think about it be that learning abstractions at different levels requires supervision to induce enough variation so that those levels have distinguishable predictive consequences?
@jchencxh E.g., could MAE’s limitation be that random masking in pixel space often doesn’t sufficiently separate high-level semantic explanations from lower-level ones, since ``p(masked pixels∣visible pixels)'' can often be predicted well without resolving the higher-level structure?
Image-based stylization has been supported in InstantStyle. Found more information at https://t.co/7lhohKTTms…. We will further combine InstantID for face stylization once stars reach 1K on GitHub.
Today is #WorldTBDay. Around a billion people have died of tuberculosis since 1800. At the current rate of decline, we can expect another 100 million to die of TB in the next century. Each of those deaths would be needless.
It all reminds me of a different date: October 24th.
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Even though I know these servers have many backups, I'm still worried about losing my data (those LaTeX formulas and tables take too much time to organize).🤡