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09/09/2023
Towards Robust Model Watermark via Reducing Parametric Vulnerability
Deep neural networks are valuable assets considering their commercial be...
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06/21/2023
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning
Communication overhead is one of the major challenges in Federated Learn...
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10/14/2022
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Vision Transformers (ViTs) have recently achieved competitive performanc...
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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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10/27/2021
Adversarial Neuron Pruning Purifies Backdoored Deep Models
As deep neural networks (DNNs) are growing larger, their requirements fo...
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07/01/2020
Temporal Calibrated Regularization for Robust Noisy Label Learning
Deep neural networks (DNNs) exhibit great success on many tasks with the...
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04/13/2020
Revisiting Loss Landscape for Adversarial Robustness
The study on improving the robustness of deep neural networks against ad...
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03/26/2020
Matrix Smoothing: A Regularization for DNN with Transition Matrix under Noisy Labels
Training deep neural networks (DNNs) in the presence of noisy labels is ...
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02/14/2020
