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Neural collapse with unconstrained features
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Neural collapse is an emergent phenomenon in deep learning that was recently discovered by Papyan, Han and Donoho. We propose a simple "unconstrained features model" in which neural collapse also emerges empirically. By studying this model, we provide some explanation for the emergence of neural collapse in terms of the landscape of empirical risk.
Forward citations
Cited by 2 Pith papers
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Imitate Optimal Policy: Prevail and Induce Action Collapse in Policy Gradient
Action Collapse Policy Gradient (ACPG) fixes the action-selection layer to a simplex ETF and claims improved discrete-action RL performance, with a theory that only covers a weighted optimal-action imitation objective.
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The Exploration of Neural Collapse under Imbalanced Data
For the L-layer unconstrained feature model with bias and squared-error loss, the global minimizer's geometric structure is governed by the singular values of the matrix (I - n1^T/N)diag(√n).
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