论文标题
熟悉假设：解释深开放式方法的行为
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods
论文作者
论文摘要
在许多对象识别应用程序中，可能的类别集是一个开放集，而部署的识别系统将在训练过程中遇到属于类别的新颖对象。检测这种“新型类别”对象通常被表达为一个异常检测问题。特征矢量数据的异常检测算法将异常视为异常值，但是离群值检测在深度学习中并不很好。取而代之的是，基于视觉对象分类器的计算徽标的方法可提供最新的性能。本文提出了这样的熟悉性假设，即这些方法成功了，因为它们正在检测到缺乏熟悉的学术特征而不是新颖性的存在。这种区别很重要，因为在存在新颖性的许多情况下，基于熟悉的检测将失败。例如，当图像既包含一个新颖的对象又包含一个熟悉的对象时，熟悉度得分将很高，因此不会注意到新颖的对象。本文回顾了文献中的证据，并提供了我们自己实验的其他证据，这些证据为这一假设提供了强有力的支持。本文最后讨论了基于熟悉的检测是否是表示学习的必然结果。
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.
