Corpus ID: 56657912

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

@article{Hendrycks2019BenchmarkingNN,
  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  author={Dan Hendrycks and Thomas G. Dietterich},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.12261}
}
In this paper we establish rigorous benchmarks for image classifier robustness. [...] Key Result Together our benchmarks may aid future work toward networks that robustly generalize.Expand
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552 Citations
Highly Influential Citations
151
Background Citations
320
Methods Citations
244
Results Citations
12

552 Citations

Citation Type

Citation Type

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References

SHOWING 1-10 OF 71 REFERENCES
Measuring Neural Net Robustness with Constraints
On Detecting Adversarial Perturbations
Adversarially Robust Generalization Requires More Data
Towards Evaluating the Robustness of Neural Networks
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Improving the Robustness of Deep Neural Networks via Stability Training
Ground-Truth Adversarial Examples
Robust Physical-World Attacks on Deep Learning Models
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
...
1
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3
4
5
...