Deep Residual Flow for Out of Distribution Detection

• URL: http://arxiv.org/abs/2001.05419v3
• Date: Sun, 19 Jul 2020 17:44:12 GMT
• Title: Deep Residual Flow for Out of Distribution Detection
• Authors: Ev Zisselman and Aviv Tamar
• Abstract summary: We present a novel approach that improves upon the state-of-the-art by leveraging an expressive density model based on normalizing flows. We demonstrate the effectiveness of our method in ResNet and DenseNet architectures trained on various image datasets.
• Score: 27.218308616245164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The effective application of neural networks in the real-world relies on proficiently detecting out-of-distribution examples. Contemporary methods seek to model the distribution of feature activations in the training data for adequately distinguishing abnormalities, and the state-of-the-art method uses Gaussian distribution models. In this work, we present a novel approach that improves upon the state-of-the-art by leveraging an expressive density model based on normalizing flows. We introduce the residual flow, a novel flow architecture that learns the residual distribution from a base Gaussian distribution. Our model is general, and can be applied to any data that is approximately Gaussian. For out of distribution detection in image datasets, our approach provides a principled improvement over the state-of-the-art. Specifically, we demonstrate the effectiveness of our method in ResNet and DenseNet architectures trained on various image datasets. For example, on a ResNet trained on CIFAR-100 and evaluated on detection of out-of-distribution samples from the ImageNet dataset, holding the true positive rate (TPR) at $95\%$, we improve the true negative rate (TNR) from $56.7\%$ (current state-of-the-art) to $77.5\%$ (ours).

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