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09/04/2023
Safe and Robust Watermark Injection with a Single OoD Image
Training a high-performance deep neural network requires large amounts o...
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04/06/2023
Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
Graph neural networks (GNNs) have been widely applied to learning over g...
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10/12/2022
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork
Deep neural networks (DNNs) are vulnerable to backdoor attacks. Previous...
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07/04/2022
How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts
Increasing concerns have been raised on deep learning fairness in recent...
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07/04/2022
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
Existing out-of-distribution (OOD) detection methods are typically bench...
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07/04/2022
Removing Batch Normalization Boosts Adversarial Training
Adversarial training (AT) defends deep neural networks against adversari...
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03/18/2022
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
Federated learning (FL) provides a distributed learning framework for mu...
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10/26/2021
AugMax: Adversarial Composition of Random Augmentations for Robust Training
Data augmentation is a simple yet effective way to improve the robustnes...
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06/18/2021
Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning
Federated learning (FL) emerges as a popular distributed learning schema...
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06/09/2021
Practical Machine Learning Safety: A Survey and Primer
The open-world deployment of Machine Learning (ML) algorithms in safety-...
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05/14/2021
Troubleshooting Blind Image Quality Models in the Wild
Recently, the group maximum differentiation competition (gMAD) has been ...
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10/22/2020
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Adversarial training and its many variants substantially improve deep ne...
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08/25/2020
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Generative adversarial networks (GANs) have gained increasing popularity...
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06/15/2020
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
The compression of Generative Adversarial Networks (GANs) has lately dra...
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02/25/2020
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
The learning of hierarchical representations for image classification ha...
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02/24/2020
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Deep networks were recently suggested to face the odds between accuracy ...
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06/12/2019
Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset
This paper aims to boost privacy-preserving visual recognition, an incre...
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02/10/2019