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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
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(2014) · Cited by 33,419
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Self-citationIntriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
Self-citationDeep neural networks are easily fooled: High confidence predictions for unrecognizable images
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Going Deeper with Convolutions
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Learning Fine-Grained Image Similarity with Deep Ranking
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Cited by
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Akhtar, N., & Mian, A. (2018) · IEEE Access · doi:10.1109/access.2018.2807385 · Cited by 2,021
Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., & Jordan, M. I. (2019) · arXiv · doi:10.48550/arxiv.1901.08573 · Cited by 1,560
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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- Zheng, S., Song, Y., Leung, T., & Goodfellow, I. (2016). Improving the Robustness of Deep Neural Networks via Stability Training. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.485
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