The Difficulty of Training Sparse Neural Networks

Utku Evci; Fabian Pedregosa; Aidan Gomez; Erich Elsen

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arXiv · 1906.10732

The Difficulty of Training Sparse Neural Networks

Utku Evci·Fabian Pedregosa·Aidan Gomez·Erich Elsen

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Abstract

We investigate the difficulties of training sparse neural networks and make new observations about optimization dynamics and the energy landscape within the sparse regime. Recent work of \citep{Gale2019, Liu2018} has shown that sparse ResNet-50 architectures trained on ImageNet-2012 dataset converge to solutions that are significantly worse than those found by pruning. We show that, despite the failure of optimizers, there is a linear path with a monotonically decreasing objective from the initialization to the "good" solution. Additionally, our attempts to find a decreasing objective path from "bad" solutions to the "good" ones in the sparse subspace fail. However, if we allow the path to traverse the dense subspace, then we consistently find a path between two solutions. These findings suggest traversing extra dimensions may be needed to escape stationary points found in the sparse subspace.

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Utku Evci, Fabian Pedregosa, Aidan Gomez, Erich Elsen. 2019-06-25. The Difficulty of Training Sparse Neural Networks. https://arxiv.org/abs/1906.10732

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