论文标题
层自适应深度神经网络用于分布外检测
Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
论文作者
论文摘要
在深神经网络（DNN）的正向传球中，输入逐渐从低级特征转变为高级概念标签。尽管不同层的特征可以总结不同级别输入的重要因素，但现代分布（OOD）检测方法主要集中在使用其结尾层特征上。在本文中，我们为DNN提出了一个新型的层 - 自适应OOD检测框架（LA-OOD），该框架可以充分利用中间层的输出。具体而言，我们没有在固定端层训练统一的OOD检测器，而是在中间层同时训练多个单级SVM OOD检测器，以利用在DNN变化深度上编码的全光谱特性。我们制定了一个简单而有效的层自适应政策，以确定检测每个潜在OOD示例的最佳层。 LA-OOD可以应用于任何现有的DNN，并且在培训期间不需要访问OOD样品。我们的实验使用了三个不同的深度和体系结构的DNN，表明LA-OOD对不同复杂性的OOD具有强大的功能，并且可以在某些现实世界中的一些数据集中大幅度优于最先进的竞争对手。
During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels. While features at different layers could summarize the important factors of the inputs at varying levels, modern out-of-distribution (OOD) detection methods mostly focus on utilizing their ending layer features. In this paper, we proposed a novel layer-adaptive OOD detection framework (LA-OOD) for DNNs that can fully utilize the intermediate layers' outputs. Specifically, instead of training a unified OOD detector at a fixed ending layer, we train multiple One-Class SVM OOD detectors simultaneously at the intermediate layers to exploit the full spectrum characteristics encoded at varying depths of DNNs. We develop a simple yet effective layer-adaptive policy to identify the best layer for detecting each potential OOD example. LA-OOD can be applied to any existing DNNs and does not require access to OOD samples during the training. Using three DNNs of varying depth and architectures, our experiments demonstrate that LA-OOD is robust against OODs of varying complexity and can outperform state-of-the-art competitors by a large margin on some real-world datasets.
