How You Start Matters for Generalization

• URL: http://arxiv.org/abs/2206.08558v1
• Date: Fri, 17 Jun 2022 05:30:56 GMT
• Title: How You Start Matters for Generalization
• Authors: Sameera Ramasinghe, Lachlan MacDonald, Moshiur Farazi, Hemanth Sartachandran, Simon Lucey
• Abstract summary: We show that the generalization of neural networks is heavily tied to their initializes. We make a case against the controversial flat-minima conjecture.
• Score: 26.74340246715699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Characterizing the remarkable generalization properties of over-parameterized neural networks remains an open problem. In this paper, we promote a shift of focus towards initialization rather than neural architecture or (stochastic) gradient descent to explain this implicit regularization. Through a Fourier lens, we derive a general result for the spectral bias of neural networks and show that the generalization of neural networks is heavily tied to their initialization. Further, we empirically solidify the developed theoretical insights using practical, deep networks. Finally, we make a case against the controversial flat-minima conjecture and show that Fourier analysis grants a more reliable framework for understanding the generalization of neural networks.

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