The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Improving the Robustness of Deep Neural Networks via Stability Training
Open accessAbstract
In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such as compression, rescaling, and cropping. We validate our method by stabilizing the state of-the-art Inception architecture [11] against these types of distortions. In addition, we demonstrate that our stabilized model gives robust state-of-the-art performance on largescale near-duplicate detection, similar-image ranking, and classification on noisy datasets.
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Cites
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Self-citationIntriguing properties of neural networks
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Self-citationDeep neural networks are easily fooled: High confidence predictions for unrecognizable images
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Cited by
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
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Theoretically principled trade-off between robustness and accuracy
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A survey on deep learning and its applications
Dong, S., Wang, P., & Abbas, K. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100379 · Cited by 1,544
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Hendrycks, D., & Dietterich, T. (2019) · arXiv · doi:10.48550/arxiv.1903.12261 · Cited by 1,335
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Carlini, N., & Wagner, D. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140444 · Cited by 966
Metadata
- Authors
- Zheng, S., Song, Y., Leung, T., & Goodfellow, I.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4480-4488
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2342045095
- Cited by
- 624
- References
- 14
- FWCI
- 18.21
- Type
- conference paper
- Language
- en
- Open access
- green
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