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Found 6 papers, 4 papers with code
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training
Several recent studies have shown that the use of extra in-distribution data can lead to a high level of adversarial robustness.
Dataset Condensation with Contrastive Signals
However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset.
Biased Multi-Domain Adversarial Training
Several recent studies have shown that the use of extra in-distribution data can lead to a high level of adversarial robustness.
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues.
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
In this paper, we identify Adversarial Feature Overfitting (AFO), which may cause poor adversarially robust generalization, and we show that adversarial training can overshoot the optimal point in terms of robust generalization, leading to AFO in our simple Gaussian model.
Computation-Efficient Quantization Method for Deep Neural Networks
We present a non-intrusive quantization technique based on re-training the full precision model, followed by directly optimizing the corresponding binary model.
