34 citations · 130 across the 35 of their papers we have counts for
13 papers · 1 filter
Unifying Perplexing Behaviors in Modified BP Attributions through Alignment Perspective
Guanhua Zheng, Jitao Sang, Changsheng Xu
Attributions aim to identify input pixels that are relevant to the decision-making process. A popular approach involves using modified backpropagation (BP) rules to reverse decisio…
Benign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features
Yi Zhang, Jitao Sang, Junyang Wang +2
Machine learning models often learn to make predictions that rely on sensitive social attributes like gender and race, which poses significant fairness risks, especially in societa…
Evaluation and Analysis of Hallucination in Large Vision-Language Models
Junyang Wang, Yiyang Zhou, Guohai Xu +9
Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many…
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks
Jiaming Zhang, Lingyu Qiu, Qi Yi +4
The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks poses a significant challenge to their deployment in safety-critical applications. While extensive research…
Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber
Rui Hu, Yahan Tu, Jitao Sang
Neural networks often learn spurious correlations when exposed to biased training data, leading to poor performance on out-of-distribution data. A biased dataset can be divided, ac…
Backdoor for Debias: Mitigating Model Bias with Backdoor Attack-based Artificial Bias
Shangxi Wu, Qiuyang He, Jian Yu +1
With the swift advancement of deep learning, state-of-the-art algorithms have been utilized in various social situations. Nonetheless, some algorithms have been discovered to exhib…
Fair Visual Recognition via Intervention with Proxy Features
Yi Zhang, Jitao Sang, Junyang Wang
Deep learning models often learn to make predictions that rely on sensitive social attributes like gender and race, which poses significant fairness risks, especially in societal a…
Towards Adversarial Attack on Vision-Language Pre-training Models
Jiaming Zhang, Qi Yi, Jitao Sang
While vision-language pre-training model (VLP) has shown revolutionary improvements on various vision-language (V+L) tasks, the studies regarding its adversarial robustness remain…
Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data
Rui Hu, Jitao Sang, Jinqiang Wang +1
Deep network models perform excellently on In-Distribution (ID) data, but can significantly fail on Out-Of-Distribution (OOD) data. While developing methods focus on improving OOD…
An Experimental Study of Semantic Continuity for Deep Learning Models
Shangxi Wu, Dongyuan Lu, Xian Zhao +2
Deep learning models suffer from the problem of semantic discontinuity: small perturbations in the input space tend to cause semantic-level interference to the model output. We arg…
MMCGAN: Generative Adversarial Network with Explicit Manifold Prior
Guanhua Zheng, Jitao Sang, Changsheng Xu
Generative Adversarial Network(GAN) provides a good generative framework to produce realistic samples, but suffers from two recognized issues as mode collapse and unstable training…
A Generalization Theory based on Independent and Task-Identically Distributed Assumption
Guanhua Zheng, Jitao Sang, Houqiang Li +2
Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which resul…
Understanding Deep Learning Generalization by Maximum Entropy
Guanhua Zheng, Jitao Sang, Changsheng Xu
Deep learning achieves remarkable generalization capability with overwhelming number of model parameters. Theoretical understanding of deep learning generalization receives recent…