Author

Shuhai Zhang

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Open access Dec 2024

Adversarial Purification by Consistency-Aware Latent Space Optimization on Data Manifolds

Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predictions. Adversarial purification has been an effective means to improve DNNs robustness by removing these perturbations before feeding the da...

Shu-Hai Zhang, Jia-Hao Yang, Hui Luo et al. · IEEE Transactions on Pattern... · 0 citations

#machine learning Review Sep 2026

A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference

The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a growing body of work studies how models can refine their behavior on the fly by exploiting test-time information and additional computation...

Shuai-Cheng Niu, Guo-Hao Chen, Yaofo Chen et al. · 2 citations

Open access Aug 2026

Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds.

This paper reveals that samples generated by a well-trained generative model are close to clean ones but far from adversarial ones, and proposes Consistency Model-based Adversarial Purification (CMAP), which optimizes vectors within the latent space of a pre-trained consistency model to generate samples for restoring c...

Shuhai Zhang, Jiahao Yang, Hui Luo et al. · IEEE Transactions on Pattern... · 0 citations