Publication

The Generalization-Stability Tradeoff In Neural Network Pruning

Conference on Neural Information Processing Systems (NeurIPS)


Abstract

Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting. This motivation is particularly relevant given the perhaps surprising observation that a wide variety of pruning approaches increase test accuracy despite sometimes massive reductions in parameter counts. To better understand this phenomenon, we analyze the behavior of pruning over the course of training, finding that pruning’s benefit to generalization increases with pruning’s instability (defined as the drop in test accuracy immediately following pruning). We demonstrate that this “generalization-stability tradeoff” is present across a wide variety of pruning settings and propose a mechanism for its cause: pruning regularizes similarly to noise injection. Supporting this, we find less pruning stability leads to more model flatness and the benefits of pruning do not depend on permanent parameter removal. These results explain the compatibility of pruning-based generalization improvements and the high generalization recently observed in overparameterized networks.

Our code is available at: https://github.com/bbartoldson/GeneralizationStabilityTradeoff

Related Publications

All Publications

SIGGRAPH - August 9, 2021

ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation

He Zhang, Yuting Ye, Takaaki Shiratori, Taku Komura

SIGGRAPH - August 9, 2021

Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

Jungdam Won, Deepak Gopinath, Jessica Hodgins

CVPR - June 20, 2021

Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

Yanghao Li, Tushar Nagarajan, Bo Xiong, Kristen Grauman

ICML - July 18, 2021

Align, then memorise: the dynamics of learning with feedback alignment

Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy