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Compact diffusion models can now leverage the power of high-capacity Teachers without architectural changes, achieving remarkable performance gains.
FaceMoE achieves unprecedented performance in low-resolution face recognition by leveraging a dynamic Mixture of Experts architecture that promotes specialization and preserves pretrained knowledge.
Models may score well on benchmarks but often fail to meet strict perceptual requirements, revealing a hidden brittleness in multimodal evaluations.
