Name
Abstract | ||
|---|---|---|
Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to the input, which undermines their true practicality. Recent works have increased the robustness of DNNs by fitting networks using adversarially-perturbed training samples, but the improved performance can still be far below the performance seen in non-adversarial settings. A significant portion of this gap can be attributed to the decrease in generalization performance due to adversarial training. In this work, we extend the notion of margin loss to adversarial settings and bound the generalization error for DNNs trained under several well-known gradient-based attack schemes, motivating an effective regularization scheme based on spectral normalization of the DNNu0027s weight matrices. We also provide a computationally-efficient method for normalizing the spectral norm of convolutional layers with arbitrary stride and padding schemes in deep convolutional networks. We evaluate the power of spectral normalization extensively on combinations of datasets, network architectures, and adversarial training schemes. The code is available at this https URL. |
Year | Venue | Field |
|---|---|---|
2018 | international conference on learning representations | Normalization (statistics),Network architecture,Supervised learning,Matrix norm,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Padding,Machine learning,Mathematics,Adversarial system |
DocType | Volume | Citations |
Journal | abs/1811.07457 | 5 |
PageRank | References | Authors |
0.40 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farzan Farnia | 1 | 53 | 6.45 |
| Jesse Zhang | 2 | 10 | 4.58 |
| David Tse | 3 | 10182 | 1122.05 |