In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories unseen during training. Detecting such "novel category" objects is usually formulated as an anomaly detection problem. Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. Instead, methods based on the computed logits of visual object classifiers give state-of-the-art performance. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. This distinction is important, because familiarity-based detection will fail in many situations where novelty is present. For example when an image contains both a novel object and a familiar one, the familiarity score will be high, so the novel object will not be noticed. The paper reviews evidence from the literature and presents additional evidence from our own experiments that provide strong support for this hypothesis. The paper concludes with a discussion of whether familiarity-based detection is an inevitable consequence of representation learning.
翻译:在许多对象识别应用程序中, 一组可能的分类是开放式的, 部署的识别系统将遇到属于训练期间看不见的类别的新东西。 检测这类“ 新分类” 对象通常被设计成异常检测问题。 特性矢量数据的异常检测算法将异常作为外向值, 但外向值检测在深层学习中效果不佳。 相反, 基于视觉对象分类器计算日志的方法提供了最先进的性能。 本文提出这些方法之所以成功, 是因为它们正在发现缺乏熟悉的学习特征, 而不是新颖性的存在。 这一区分很重要, 因为在存在新颖性的许多情况下, 以熟悉为基础的检测将会失败。 例如, 当图像既包含新颖的物体, 也包含熟悉度, 熟悉度将会很高, 因此新对象不会被注意。 本文回顾了文献中的证据, 并提供了我们自身实验中的其他证据, 有力地支持这一假设。 论文最后讨论了熟悉度测算是否是陈述学习的必然结果。