用多层径向基网络一步完成分类与异常检测，效果媲美主流方法。
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection
- 设计可训练的多层径向基网络，融合分类与异常检测
- 提出新型抑制机制，提升对分布外样本的识别能力
- 兼容预训练模型，适合需要高效异常检测的场景
现有的分布外（OOD）检测方法通常使用独立于分类的评分机制来判断输入是否为分布外。本文洞察到，可通过神经网络架构将分类与OOD检测合并为单步操作。径向基函数网络（RBFN）天生具备关联分类置信度与OOD检测的能力，但因难以实现多层训练而逐渐被弃用。本文提出多层径向基函数网络（MLRBFN），可有效训练。为确保其在OOD检测中的有效性，我们引入一种新颖的抑制机制。实验表明，无论作为独立分类器或作为预训练特征提取器的头部，MLRBFN在多个基准上均达到与主流方法相当的性能，展现出良好的应用前景。
原文摘要 · Abstract (English)
Existing methods for out-of-distribution (OOD) detection use various techniques to produce a score, separate from classification, that determines how ``OOD'' an input is. Our insight is that OOD detection can be simplified by using a neural network architecture which can effectively merge classification and OOD detection into a single step. Radial basis function networks (RBFNs) inherently link classification confidence and OOD detection; however, these networks have lost popularity due to the difficult of training them in a multi-layer fashion. In this work, we develop a multi-layer radial basis function network (MLRBFN) which can be easily trained. To ensure that these networks are also effective for OOD detection, we develop a novel depression mechanism. We apply MLRBFNs as standalone classifiers and as heads on top of pretrained feature extractors, and find that they are competitive with commonly used methods for OOD detection. Our MLRBFN architecture demonstrates a promising new direction for OOD detection methods.
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