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05/28/2023
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
Label-noise learning (LNL) aims to increase the model's generalization g...
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04/30/2023
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks
Adversarial training (AT) is a robust learning algorithm that can defend...
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04/30/2023
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
Adversarial contrastive learning (ACL), without requiring labels, incorp...
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02/08/2023
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
Adversarial contrastive learning (ACL) does not require expensive data a...
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02/06/2023
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
While leveraging additional training data is well established to improve...
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11/01/2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Adversarial training (AT) with imperfect supervision is significant but ...
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06/08/2022
Accelerating Score-based Generative Models for High-Resolution Image Synthesis
Score-based generative models (SGMs) have recently emerged as a promisin...
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03/25/2022
WaveFuzz: A Clean-Label Poisoning Attack to Protect Your Voice
People are not always receptive to their voice data being collected and ...
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02/22/2022
On the Effectiveness of Adversarial Training against Backdoor Attacks
DNNs' demand for massive data forces practitioners to collect data from ...
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02/07/2022
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests
Non-parametric two-sample tests (TSTs) that judge whether two sets of sa...
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01/12/2022
Towards Adversarially Robust Deep Image Denoising
This work systematically investigates the adversarial robustness of deep...
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07/21/2021
DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework
In multimodal tasks, we find that the importance of text and image modal...
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07/17/2021
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition
In fine-grained image recognition (FGIR), the localization and amplifica...
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06/09/2021
Reliable Adversarial Distillation with Unreliable Teachers
In ordinary distillation, student networks are trained with soft labels ...
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05/31/2021
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training?
Adversarial training (AT) based on minimax optimization is a popular lea...
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02/10/2021
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
We investigate the adversarial robustness of CNNs from the perspective o...
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02/06/2021
Understanding the Interaction of Adversarial Training with Noisy Labels
Noisy labels (NL) and adversarial examples both undermine trained models...
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02/03/2021
Learning Diverse-Structured Networks for Adversarial Robustness
In adversarial training (AT), the main focus has been the objective and ...
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10/22/2020
Maximum Mean Discrepancy is Aware of Adversarial Attacks
The maximum mean discrepancy (MMD) test, as a representative two-sample ...
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10/05/2020
Geometry-aware Instance-reweighted Adversarial Training
In adversarial machine learning, there was a common belief that robustne...
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04/22/2020
Hierarchically Fair Federated Learning
Federated learning facilitates collaboration among self-interested agent...
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02/26/2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Adversarial training based on the minimax formulation is necessary for o...
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11/20/2019
Where is the Bottleneck of Adversarial Learning with Unlabeled Data?
Deep neural networks (DNNs) are incredibly brittle due to adversarial ex...
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02/28/2019
Towards Robust ResNet: A Small Step but A Giant Leap
This paper presents a simple yet principled approach to boosting the rob...
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09/27/2018
