NeurIPS 2020poster27 citations
Adversarial robustness via robust low rank representations
Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan
Abstract
Adversarial robustness measures the susceptibility of a classifier to imperceptible perturbations made to the inputs at test time. In this work we highlight the benefits of natural low rank representations that often exist for real data such as images, for training neural networks with certified robustness guarantees.
BibTeX
@inproceedings{NEURIPS2020_837a7924,
author = {Awasthi, Pranjal and Jain, Himanshu and Rawat, Ankit Singh and Vijayaraghavan, Aravindan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {11391--11403},
publisher = {Curran Associates, Inc.},
title = {Adversarial robustness via robust low rank representations},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/837a7924b8c0aa866e41b2721f66135c-Paper.pdf},
volume = {33},
year = {2020}
}