论文标题
深度剩余流量以进行分配检测
Deep Residual Flow for Out of Distribution Detection
论文作者
论文摘要
神经网络在现实世界中的有效应用依赖于熟练地检测出分布的例子。当代方法旨在对训练数据中特征激活的分布进行建模，以充分区分异常，而最先进的方法则使用高斯分布模型。在这项工作中，我们提出了一种新颖的方法，该方法通过利用基于归一化流量的表达密度模型来改善最先进的方法。我们介绍了残余流，这是一种新型的流量结构，从基本高斯分布中学习残余分布。我们的模型是一般的，可以应用于大约高斯的任何数据。对于图像数据集中的分发检测，我们的方法对最新的方法进行了有理的改进。具体而言，我们证明了方法在重新NET和DENSENET架构中的有效性。例如，在经过CIFAR-100培训的重新NET上，并对来自Imagenet数据集的分发样品进行了评估，并以95美元的$ 95 \％$持有的正率（TPR），我们将真实的负率（TNR）从56.7 \％\％\％\％\％（当前的省级ART）提高到$ 77.5.5 \ $ $ $ $（我们的％$ \％）。
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).
