Jingfeng Zhang

CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

2021 · arXiv (Cornell University)

We investigate the adversarial robustness of CNNs from the perspective of channel-wise activations. By comparing \textit{non-robust} (normally trained) and \textit{robustified} (adversarially trained) models, we observe that adversarial training (AT) robustifies CNNs by aligning the channel-wise …

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning

2024 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Label-noise learning (LNL) aims to increase the model's generalization given training data with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different label noise types, ranging from class-conditional to instance-dependent noises. In this …

Balancing Invariant and Specific Knowledge for Domain Generalization with Online Knowledge Distillation

2025

Recent research has demonstrated the effectiveness of knowledge distillation in Domain Generalization. However, existing approaches often overlook domain-specific knowledge and rely on an offline distillation strategy, limiting the effectiveness of knowledge transfer. To address these …