Revisiting Random Weight Perturbation for Efficiently Improving Generalization
Tao Li, Qinghua Tao, Weihao Yan, Yingwen Wu, Zehao Lei, Kun Fang, Mingzhen He, Xiaolin Huang

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• This work enhances the generalization performance of random weight perturbation from the perspective of convergence and perturbation generation, and shows that it can achieve more efficient generalization improvement than adversarial weight perturbation in SAM, especially on large-scale problems.

Friendly Sharpness-Aware Minimization
Tao Li, Pan Zhou, Zhengbao He, Xinwen Cheng, Xiaolin Huang

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• This work uncovers that the full gradient component in SAM’s adversarial perturbation does not contribute to generalization and, in fact, has undesirable effects. We propose an efficient variant to mitigate these effects and enhance the generalization performance.

Trainable Weight Averaging: Efficient Training by Optimizing Historical Solutions
Tao Li, Zhehao Huang, Qinghua Tao, Yingwen Wu, Xiaolin Huang

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• This work introduces trainable weight averaging to average the historical solutions during the DNN training process to achieve efficient training and better performance.

Subspace Adversarial Training
Tao Li, Yingwen Wu, Sizhe Chen, Kun Fang, Xiaolin Huang

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• This work proposes subspace training as a method to address the overfitting issues in single and multi-step adversarial training, known as catastrophic and robust overfittings. We achieve efficient and stable single-step adversarial training with comparable robustness performance of multi-step methods.

Low Dimensional Trajectory Hypothesis is True: DNNs can be Trained in Tiny Subspaces
Tao Li, Lei Tan, Zhehao Huang, Qinghua Tao, Yipeng Liu, Xiaolin Huang

Code

• This work explores the low-dimensional characteristics of DNN training trajectories and proposes a dimension reduction method for training DNNs within a lower-dimensional subspace. This approach has the potential to reduce training costs and enhance model robustness.