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Modern deep neural networks for classification usually jointly learn a backbone for representation and a linear classifier to output the logit of each class.
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Disentangling trainability and generalization in deep neural networks
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Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
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mixup: Beyond empirical risk minimization
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Maximally compact and separated features with regular polytope networks
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Regular polytope networks
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Improving calibration for long-tailed recognition
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A geometric analysis of neural collapse with unconstrained features
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Neural collapse under mse loss: Proximity to and dynamics on the central path
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An unconstrained layer-peeled perspective on neural collapse
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Extended unconstrained features model for exploring deep neural collapse
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