34 citations · 174 across the 23 of their papers we have counts for
53 papers
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
Xin Li, Yao Qiang, Chengyin Li +2
This work tackles a central machine learning problem of performance degradation on out-of-distribution (OOD) test sets. The problem is particularly salient in medical imaging based…
Reverse Engineering of Imperceptible Adversarial Image Perturbations
Yifan Gong, Yuguang Yao, Yize Li +4
It has been well recognized that neural network based image classifiers are easily fooled by images with tiny perturbations crafted by an adversary. There has been a vast volume of…
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Shuai Zhang, Meng Wang, Sijia Liu +2
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited. Despite empirical successes,…
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
Ching-Yun Ko, Jeet Mohapatra, Sijia Liu +3
As a seminal tool in self-supervised representation learning, contrastive learning has gained unprecedented attention in recent years. In essence, contrastive learning aims to leve…
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
Lijie Fan, Sijia Liu, Pin-Yu Chen +2
Contrastive learning (CL) can learn generalizable feature representations and achieve the state-of-the-art performance of downstream tasks by finetuning a linear classifier on top…
Certifiably Robust Interpretation via Renyi Differential Privacy
Ao Liu, Xiaoyu Chen, Sijia Liu +2
Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of…