9 citations · 29 across the 22 of their papers we have counts for
4 papers · 1 filter
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting
Chengyu Dong, Liyuan Liu, Jingbo Shang
We show that label noise exists in adversarial training. Such label noise is due to the mismatch between the true label distribution of adversarial examples and the label inherited…
BFClass: A Backdoor-free Text Classification Framework
Zichao Li, Dheeraj Mekala, Chengyu Dong +1
Backdoor attack introduces artificial vulnerabilities into the model by poisoning a subset of the training data via injecting triggers and modifying labels. Various trigger design…
"Average" Approximates "First Principal Component"? An Empirical Analysis on Representations from Neural Language Models
Zihan Wang, Chengyu Dong, Jingbo Shang
Contextualized representations based on neural language models have furthered the state of the art in various NLP tasks. Despite its great success, the nature of such representatio…
Data Quality Matters For Adversarial Training: An Empirical Study
Chengyu Dong, Liyuan Liu, Jingbo Shang
Multiple intriguing problems are hovering in adversarial training, including robust overfitting, robustness overestimation, and robustness-accuracy trade-off. These problems pose g…