Diffusion models tolerate pruning up to 90% of training data without FID degradation, and cluster-center selection in CLIP/DINO embedding space beats established gradient-based pruning methods.
Ethi- cal considerations and policy interventions concern- ing the impact of generative ai tools in the economy and in society
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Data Pruning in Generative Diffusion Models
Diffusion models tolerate pruning up to 90% of training data without FID degradation, and cluster-center selection in CLIP/DINO embedding space beats established gradient-based pruning methods.