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- Shu‐Tao XiaSuggested from co-authorship
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Works137 from public data
- Symmetric Cross Entropy for Robust Learning With Noisy Labels1,212
The proposed Symmetric cross entropy Learning (SL) approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels, and empirically shows that SL outperforms state-of-the-art methods.
- Adversarial Weight Perturbation Helps Robust Generalization923
This paper proposes a simple yet effective Adversarial Weight Perturbation (AWP) to explicitly regularize the flatness of weight loss landscape, forming a double-perturbation mechanism in the adversarial training framework that adversarially perturbs both inputs and weights.
- Improving Adversarial Robustness Requires Revisiting Misclassified Examples876
This paper proposes a new defense algorithm called MART, which explicitly differentiates the misclassified and correctly classified examples during the training, and shows that MART and its variant could significantly improve the state-of-the-art adversarial robustness.
- Understanding adversarial attacks on deep learning based medical image analysis systems567
It is found that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints, and surprisingly, medical adversarialattacks can be easily detected.
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- Iterative Learning with Open-set Noisy Labels361
A novel iterative learning framework for training CNNs on datasets with open-set noisy labels that detects noisy labels and learns deep discriminative features in an iterative fashion and designs a Siamese network to encourage clean labels and noisy labels to be dissimilar.
- Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles285
Experimental evaluation shows that, in both digital and physical-world scenarios, adversarial examples crafted by Adversarial Camouflage are well camouflaged and highly stealthy, while remaining effective in fooling state-of-the-art DNN image classifiers.
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- The EBS-A* algorithm: An improved A* algorithm for path planning146
An improved A*-based algorithm is proposed, called the EBS-A* algorithm, that introduces expansion distance, bidirectional search, and smoothing into path planning, and improves the path planning efficiency by 278% and reduces the number of critical nodes by 91.89%.
- Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap136
The theory suggests an alternative understanding of contrastive learning: the role of aligning positive samples is more like a surrogate task than an ultimate goal, and the overlapped augmented views create a ladder for Contrastive learning to gradually learn class-separated representations.
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- Decoupled Networks92
A generic decoupled learning framework which models the intra-class variation and semantic difference independently and directly learn the operator from data is proposed.
- Fight Back Against Jailbreaking via Prompt Adversarial Tuning91
This paper proposes an approach named Prompt Adversarial Tuning (PAT) that trains a prompt control attached to the user prompt as a guard prefix that is effective against both grey-box and black-box attacks, while maintaining the model's utility on the benign task and incurring only negligible computational overhead.
- CFA: Class-Wise Calibrated Fair Adversarial Training91
This paper is the first to theoretically and empirically investigate the preference of different classes for adversarial configu-rations, including perturbation margin, regularization, and weight averaging, which customizes specific training con-figurations for each class automatically.
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- What is Wrong with Perplexity for Long-context Language Modeling?77
Deeper insights are offered into the limitations of PPL and effective solutions for accurately evaluating and enhancing the long-context capabilities of LLMs are presented, leading to consistent improvements across diverse benchmarks.
- ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond67
A novel normalization layer called ContraNorm is proposed, inspired by the effectiveness of contrastive learning in preventing dimensional collapse, which implicitly shatters representations in the embedding space, leading to a more uniform distribution and a slighter dimensional collapse.
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- Self-Ensemble Adversarial Training for Improved Robustness61
This work devise a simple but powerful Self-Ensemble Adversarial Training (SEAT) method for yielding a robust classifier by averaging weights of history models and discusses the relationship between the ensemble of predictions from different adversarially trained models and the prediction of weight-ensembled models.
- Improving Query Efficiency of Black-Box Adversarial Attack61
This paper explores the distribution of adversarial examples around benign inputs with the help of image structure information characterized by a Neural Process, and proposes a Neuralprocess based black-box adversarial attack (NP-Attack) in this paper.
- Analysis and Applications of Class-wise Robustness in Adversarial Training60
This work proposes to analyze the class-wise robustness in adversarial training and proposes a simple but effective attack method, named Temperature-PGD attack, which proposes to enlarge the robustness disparity among classes with a temperature factor on the confidence distribution of each image.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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