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In this paper we address the issue of output instability of deep neuralnetworks: small perturbations in the visual input can significantly distort thefeature embeddings and output of a neural network. Such instability affectsmany deep architectures with state-of-the-art performance on a wide range ofcomputer vision tasks. We present a general stability training method tostabilize deep networks against small input distortions that result fromvarious types of common image processing, such as compression, rescaling, andcropping. We validate our method by stabilizing the state-of-the-art Inceptionarchitecture against these types of distortions. In addition, we demonstratethat our stabilized model gives robust state-of-the-art performance onlarge-scale near-duplicate detection, similar-image ranking, and classificationon noisy datasets.

Improving the Robustness of Deep Neural Networks via Stability Training