Sharpness-aware training for free

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Sharpness-aware training for free
Title:
Sharpness-aware training for free
Journal Title:
Conference on Neural Information Processing Systems
DOI:
Keywords:
Publication Date:
18 December 2022
Citation:
Du, J., Zhou, D., Feng, J., Tan, V., & Zhou, J. T. (2022). Sharpness-Aware Training for Free. In Advances in Neural Information Processing Systems (NeurIPS) 35.
Abstract:
Modern deep neural networks (DNNs) have achieved state-of-the-art performances but are typically over-parameterized. The over-parameterization may result in undesirably large generalization error in the absence of other customized training strategies. Recently, a line of research under the name of Sharpness-Aware Minimization (SAM) has shown that minimizing a sharpness measure, which reflects the geometry of the loss landscape, can significantly reduce the generalization error. However, SAM-like methods incur a two-fold computational overhead of the given base optimizer (e.g., SGD) for approximating the sharpness measure. In this paper, we propose Sharpness-Aware Training for Free, or SAF, which mitigates the sharp landscape at almost zero additional computational cost over the base optimizer. Intuitively, SAF achieves this by avoiding sudden drops in the loss in the sharp local minima throughout the trajectory of the updates of the weights. Specifically, we suggest a novel trajectory loss, based on the KL-divergence between the outputs of DNNs with the current weights and past weights, as a replacement of the SAM's sharpness measure. This loss captures the rate of change of the training loss along the model's update trajectory. By minimizing it, SAF ensures the convergence to a flat minimum with improved generalization capabilities. Extensive empirical results show that SAF minimizes the sharpness in the same way that SAM does, yielding better results on the ImageNet dataset with essentially the same computational cost as the base optimizer.
License type:
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Science and Engineering Research Council (SERC) - Central Research Fund
Grant Reference no. :

This research / project is supported by the Singapore Government’s Research, Innovation and Enterprise 2020 Plan (Advanced Manufacturing and Engineering domain) - N/A
Grant Reference no. : A18A1b0045

This research / project is supported by the National Research Foundation (NRF), Singapore - Fellowship
Grant Reference no. : A-0005077-01-00

This research / project is supported by the Singapore Ministry of Education (MOE) - AcRF Tier 1 Grant
Grant Reference no. : A-0009042-01-00 and A-8000189-01-00
Description:
ISSN:
1049-5258