论文标题
返回基础知识：重新访问分布式检测基线
Back to the Basics: Revisiting Out-of-Distribution Detection Baselines
论文作者
论文摘要
我们研究了与任何已经训练的分类器兼容的简单方法（OOD）图像检测，仅依靠其预测或学会的表示。当使用Resnet-50和Swin Transformer模型使用时，评估各种方法的OOD检测性能，我们找到了仅考虑学会表示的模型预测的方法，可以轻松地胜过模型的预测。根据我们的分析，我们主张采用其他研究中忽略的死更简单方法：仅作为OOD图像标记，其平均距离与K最近的邻居的平均距离很大（在图像分类器的表示空间中，对分布数据进行了训练）。
We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations. Evaluating the OOD detection performance of various methods when utilized with ResNet-50 and Swin Transformer models, we find methods that solely consider the model's predictions can be easily outperformed by also considering the learned representations. Based on our analysis, we advocate for a dead-simple approach that has been neglected in other studies: simply flag as OOD images whose average distance to their K nearest neighbors is large (in the representation space of an image classifier trained on the in-distribution data).
