Deep neural networks have repeatedly been shown to be non-robust to the uncertainties of the real world. Even subtle adversarial attacks and naturally occurring distribution shifts wreak havoc on systems relying on deep neural networks. In response to this, current state-of-the-art techniques use data-augmentation to enrich the training distribution of the model and consequently improve robustness to natural distribution shifts. We propose an alternative approach that allows the system to recover from distribution shifts online. Specifically, our method applies a sequence of semantic-preserving transformations to bring the shifted data closer in distribution to the training set, as measured by the Wasserstein distance. We formulate the problem of sequence selection as an MDP, which we solve using reinforcement learning. To aid in our estimates of Wasserstein distance, we employ dimensionality reduction through orthonormal projection. We provide both theoretical and empirical evidence that orthonormal projection preserves characteristics of the data at the distributional level. Finally, we apply our distribution shift recovery approach to the ImageNet-C benchmark for distribution shifts, targeting shifts due to additive noise and image histogram modifications. We demonstrate an improvement in average accuracy up to 14.21% across a variety of state-of-the-art ImageNet classifiers.

翻译：深度神经网络已被反复证明对真实世界的不确定性缺乏鲁棒性。即使是微妙的对抗攻击和自然发生的分布偏移，也会对依赖深度神经网络的系统造成严重破坏。针对这一问题，当前最先进的技术使用数据增强来丰富模型的训练分布，从而提高对自然分布偏移的鲁棒性。我们提出了一种替代方法，允许系统在线从分布偏移中恢复。具体而言，我们的方法应用一系列保持语义的变换，使偏移数据在分布上更接近训练集，这一接近程度由Wasserstein距离衡量。我们将序列选择问题建模为马尔可夫决策过程（MDP），并使用强化学习求解。为辅助Wasserstein距离的估计，我们通过正交投影进行降维。我们提供了理论和经验证据，表明正交投影能够在分布层面保留数据的特征。最后，我们将这种分布偏移恢复方法应用于针对分布偏移的ImageNet-C基准测试，重点处理由加性噪声和图像直方图修改引起的偏移。我们证明，在多种最先进的ImageNet分类器上，平均准确率可提升高达14.21%。