MUCS uses mirrored unlearning and noise-consistent skew to outperform prior TDA methods for diffusion models on three datasets.
Data attribution for text-to-image models by unlearning synthesized images.Advances in Neural In- formation Processing Systems, 37:4235–4266, 2024d
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A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.
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Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew
MUCS uses mirrored unlearning and noise-consistent skew to outperform prior TDA methods for diffusion models on three datasets.
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On the Fragility of Data Attribution When Learning Is Distributed
A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.