192 citations · 646 across the 44 of their papers we have counts for
15 papers · 1 filter
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score
Shuhai Zhang, Feng Liu, Jiahao Yang +4
Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimati…
Pareto-aware Neural Architecture Generation for Diverse Computational Budgets
Yong Guo, Yaofo Chen, Yin Zheng +5
Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world…
Efficient Test-Time Model Adaptation without Forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang +4
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particu…
AdaXpert: Adapting Neural Architecture for Growing Data
Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu +5
In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for…
Learning Defense Transformers for Counterattacking Adversarial Examples
Jincheng Li, Jiezhang Cao, Yifan Zhang +2
Deep neural networks (DNNs) are vulnerable to adversarial examples with small perturbations. Adversarial defense thus has been an important means which improves the robustness of D…
Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets
Yong Guo, Yaofo Chen, Yin Zheng +5
Designing feasible and effective architectures under diverse computation budgets incurred by different applications/devices is essential for deploying deep models in practice. Exis…