Name
Abstract | ||
|---|---|---|
Deep neural networks have recently shown impressive classification performance on a diverse set of visual tasks. When deployed in real-world (noise-prone) environments, it is equally important that these classifiers satisfy robustness guarantees: small perturbations applied to the samples should not yield significant loss to the performance of the predictor. The goal of this article is to discuss ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MSP.2017.2740965 | IEEE Signal Processing Magazine |
Keywords | Field | DocType |
Machine learning,Neural networks,Visualization,Neural networks,Classification,Robustness | Open research,Data mining,Computer science,Visualization,Transformation geometry,Random noise,Robustness (computer science),Artificial intelligence,Classifier (linguistics),Artificial neural network,Decision boundary,Machine learning | Journal |
Volume | Issue | ISSN |
34 | 6 | 1053-5888 |
Citations | PageRank | References |
21 | 0.90 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alhussein Fawzi | 1 | 766 | 36.80 |
| Seyed-Mohsen Moosavi-Dezfooli | 2 | 627 | 26.32 |
| Pascal Frossard | 3 | 3015 | 230.41 |