adversarial robustness
Improving Robustness with Adaptive Weight Decay
Deep Neural Networks (DNNs) have exceeded human capability on many computer vision tasks.
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Characterization of Overfitting in Robust Multiclass Classification
Nonetheless, modern machine learning is adaptive in its nature. Prior information about a model's performance on the test set inevitably influences
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Training on Foveated Images Improves Robustness to Adversarial Attacks
Figure 1: R-Blur adds Gaussian noise to image (a) with the fixation point (red dot) to obtain (b).
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Robust low-rank training via approximate orthonormal constraints
By modeling robustness in terms of the condition number of the neural network, we argue that this loss of robustness is due to the exploding singular values of the low-rank weight matrices.
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