WoodFisher: Efficient Second-Order Approximation for Neural Network Compression

• URL: http://arxiv.org/abs/2004.14340v5
• Date: Wed, 25 Nov 2020 17:31:09 GMT
• Title: WoodFisher: Efficient Second-Order Approximation for Neural Network Compression
• Authors: Sidak Pal Singh, Dan Alistarh
• Abstract summary: We develop a method to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression. We show how our method can be extended to take into account first-order information.
• Score: 35.45199662813043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep neural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for one-shot pruning. Further, even when iterative, gradual pruning is considered, our method results in a gain in test accuracy over the state-of-the-art approaches, for pruning popular neural networks (like ResNet-50, MobileNetV1) trained on standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as illustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher.

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