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Pretraining MIL networks with knowledge distillation from foundation models boosts performance and stability, especially in few-shot scenarios.
Deep LDA implicitly conserves a (2/L) quasi-norm in deep linear networks, revealing a novel form of implicit regularization in discriminative metric learning.
