论文标题
在Imagenet-O上的分布检测中
Out of Distribution Detection on ImageNet-O
论文作者
论文摘要
出现分布（OOD）检测是使机器学习系统健壮的关键部分。 ImageNet-O数据集是测试经训练的深神经网络的鲁棒性的重要工具，这些神经网络在各种系统和应用中广泛使用。我们旨在对Imagenet-O上的OOD检测方法进行比较分析，这是一个与Imagenet不同的标签分布的同类数据集，该数据集已创建，该数据集是为了帮助ImageNet模型的OOD检测研究。由于该数据集是相当新的，因此我们旨在为这个新颖数据集上的某些最新检测方法提供全面的基准测试。这种基准测试涵盖了各种模型体系结构，我们在不访问OOD数据的设置与不在时，基于预测分数的方法，深度生成的OOD检测方法等等。
Out of distribution (OOD) detection is a crucial part of making machine learning systems robust. The ImageNet-O dataset is an important tool in testing the robustness of ImageNet trained deep neural networks that are widely used across a variety of systems and applications. We aim to perform a comparative analysis of OOD detection methods on ImageNet-O, a first of its kind dataset with a label distribution different than that of ImageNet, that has been created to aid research in OOD detection for ImageNet models. As this dataset is fairly new, we aim to provide a comprehensive benchmarking of some of the current state of the art OOD detection methods on this novel dataset. This benchmarking covers a variety of model architectures, settings where we haves prior access to the OOD data versus when we don't, predictive score based approaches, deep generative approaches to OOD detection, and more.
