84 citations · 375 across the 68 of their papers we have counts for
32 papers · 1 filter
Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment
Xizhe Zhang, Jingfeng Zhang, Meng-Fen Chiang
Multimodal entity alignment (MMEA) is fundamental to integrating heterogeneous knowledge graphs (KGs), yet existing paradigms remain critically vulnerable to incomplete modalities…
Benign Overfitting in Adversarial Training for Vision Transformers
Jiaming Zhang, Meng Ding, Shaopeng Fu +2
Despite the remarkable success of Vision Transformers (ViTs) across a wide range of vision tasks, recent studies have revealed that they remain vulnerable to adversarial examples,…
Controllable Concept Bottleneck Models
Hongbin Lin, Chenyang Ren, Juangui Xu +7
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most prev…
Robust Learning of Diffusion Models with Extremely Noisy Conditions
Xin Chen, Gillian Dobbie, Xinyu Wang +3
Conditional diffusion models have the generative controllability by incorporating external conditions. However, their performance significantly degrades with noisy conditions, such…
Adversarial Preference Learning for Robust LLM Alignment
Yuanfu Wang, Pengyu Wang, Chenyang Xi +13
Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to th…
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui +10
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learn…
One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
Jiacheng Zhang, Benjamin I. P. Rubinstein, Jingfeng Zhang +1
Statistical adversarial data detection (SADD) detects whether an upcoming batch contains adversarial examples (AEs) by measuring the distributional discrepancies between clean exam…
Accurate Forgetting for Heterogeneous Federated Continual Learning
Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang +6
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging F…
Dissecting Representation Misalignment in Contrastive Learning via Influence Function
Lijie Hu, Chenyang Ren, Huanyi Xie +5
Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled…
Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
Yukai Xu, Jingfeng Zhang, Yujie Gu
In the realm of healthcare where decentralized facilities are prevalent, machine learning faces two major challenges concerning the protection of data and models. The data-level ch…
Balancing Similarity and Complementarity for Federated Learning
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi +5
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively using data while maintaining user privacy. One key challenge in FL is managing statisti…
Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models
Zihao Luo, Xilie Xu, Feng Liu +3
Low-rank adaptation (LoRA) is an efficient strategy for adapting latent diffusion models (LDMs) on a private dataset to generate specific images by minimizing the adaptation loss.…
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework
Xilie Xu, Jingfeng Zhang, Mohan Kankanhalli
Robust Fine-Tuning (RFT) is a low-cost strategy to obtain adversarial robustness in downstream applications, without requiring a lot of computational resources and collecting signi…
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
Jingfeng Zhang, Bo Song, Haohan Wang +4
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 diff…
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
Xilie Xu, Jingfeng Zhang, Feng Liu +2
Adversarial contrastive learning (ACL) is a technique that enhances standard contrastive learning (SCL) by incorporating adversarial data to learn a robust representation that can…
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
Xilie Xu, Jingfeng Zhang, Feng Liu +2
Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a…
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Jianan Zhou, Jianing Zhu, Jingfeng Zhang +4
Adversarial training (AT) with imperfect supervision is significant but receives limited attention. To push AT towards more practical scenarios, we explore a brand new yet challeng…
On the Effectiveness of Adversarial Training against Backdoor Attacks
Yinghua Gao, Dongxian Wu, Jingfeng Zhang +4
DNNs' demand for massive data forces practitioners to collect data from the Internet without careful check due to the unacceptable cost, which brings potential risks of backdoor at…
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
Xilie Xu, Jingfeng Zhang, Feng Liu +2
Non-parametric two-sample tests (TSTs) that judge whether two sets of samples are drawn from the same distribution, have been widely used in the analysis of critical data. People t…
Reliable Adversarial Distillation with Unreliable Teachers
Jianing Zhu, Jiangchao Yao, Bo Han +6
In ordinary distillation, student networks are trained with soft labels (SLs) given by pretrained teacher networks, and students are expected to improve upon teachers since SLs are…
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels
Jingfeng Zhang, Xilie Xu, Bo Han +4
Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly…
Guided Interpolation for Adversarial Training
Chen Chen, Jingfeng Zhang, Xilie Xu +4
To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data. However, as the training progresses…
Understanding the Interaction of Adversarial Training with Noisy Labels
Jianing Zhu, Jingfeng Zhang, Bo Han +5
Noisy labels (NL) and adversarial examples both undermine trained models, but interestingly they have hitherto been studied independently. A recent adversarial training (AT) study…
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
Hanshu Yan, Jingfeng Zhang, Gang Niu +3
We investigate the adversarial robustness of CNNs from the perspective of channel-wise activations. By comparing \textit{non-robust} (normally trained) and \textit{robustified} (ad…