mlsanityData to 7 Oct 2026

Improving the Robustness of Deep Neural Networks via Stability Training

arXiv 1604.04326 ↗First posted 15 Apr 2016cs.CV cs.LGcited by 156

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Abstract (arXiv)

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 against these types of distortions. In addition, we demonstrate that our stabilized model gives robust state-of-the-art performance on large-scale near-duplicate detection, similar-image ranking, and classification on noisy datasets.

Key facts. Cited by 156 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2016.

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Citing papers per year: 2021: 53; 2022: 41; 2023: 28; 2024: 20; 2025: 9; 2026: 5.

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