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Peking University
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Lever-Edit shows that you can effectively optimize image editing policies using T2I rewards, bypassing the need for costly editing-specific rewards altogether.
Bridging the gap between ANNs and SNNs could revolutionize federated learning on resource-constrained devices, achieving high accuracy without sacrificing efficiency.
