Invariance-Inducing Regularization Using Worst-Case Transformations Suffices to Boost Accuracy and Spatial Robustness
Abstract
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness). Evaluated on these adversarially transformed examples, we demonstrate that adding regularization on top of standard or adversarial training reduces the relative error by 20% for CIFAR10 without increasing the computational cost. This outperforms handcrafted networks that were explicitly designed to be spatial-equivariant. Furthermore, we observe for SVHN, known to have inherent variance in orientation, that robust training also improves standard accuracy on the test set.
Cite
Text
Yang et al. "Invariance-Inducing Regularization Using Worst-Case Transformations Suffices to Boost Accuracy and Spatial Robustness." ICML 2019 Workshops: Deep_Phenomena, 2019.Markdown
[Yang et al. "Invariance-Inducing Regularization Using Worst-Case Transformations Suffices to Boost Accuracy and Spatial Robustness." ICML 2019 Workshops: Deep_Phenomena, 2019.](https://mlanthology.org/icmlw/2019/yang2019icmlw-invarianceinducing/)BibTeX
@inproceedings{yang2019icmlw-invarianceinducing,
title = {{Invariance-Inducing Regularization Using Worst-Case Transformations Suffices to Boost Accuracy and Spatial Robustness}},
author = {Yang, Fanny and Wang, Zuowen and Heinze-Deml, Christina},
booktitle = {ICML 2019 Workshops: Deep_Phenomena},
year = {2019},
url = {https://mlanthology.org/icmlw/2019/yang2019icmlw-invarianceinducing/}
}