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Allocating generative capacity based on local complexity can lead to a 35% boost in synthesis quality without increasing inference costs.
Retrieval augmentation lets head avatars handle novel expressions better by mixing in similar expressions from a large unlabeled dataset during training, boosting generalization without extra labels or architecture changes.
Cycle-consistent training unlocks robust layered image decomposition in diffusion models, even with complex interactions like shading and reflections.
