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.

本研究解决了深度神经网络输出不稳定的问题，通过提出一种稳定训练方法，使其能够应对常见图像处理带来的输入扭曲，并在大规模近似重复检测、类图像排序和嘈杂数据集分类等方面取得了鲁棒且最先进的性能。