Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. Prior work in deep AD is predominantly based on a familiarity hypothesis, where familiar features serve as the reference in a pre-trained embedding space. While this strategy has proven highly successful, it turns out that it causes consistent false negatives when anomalies consist of truly novel features that are not well captured by the pre-trained encoding. We propose a novel approach to AD using explainability to capture novel features as unexplained observations in the input space. We achieve strong performance across a wide range of anomaly benchmarks by combining similarity and novelty in a hybrid approach. Our approach establishes a new state-of-the-art across multiple benchmarks, handling diverse anomaly types while eliminating the need for expensive background models and dense matching. In particular, we show that by taking account of novel features, we reduce false negative anomalies by up to 40% on challenging benchmarks compared to the state-of-the-art. Our method gives visually inspectable explanations for pixel-level anomalies.

翻译：异常检测（AD）是一项关键任务，旨在识别不符合正常学习模型的观测值。先前深度AD研究主要基于一种熟悉性假设，以预训练嵌入空间中的熟悉特征作为参考标准。尽管该策略已被证明极为成功，但当异常由未被预训练编码充分捕获的真正新颖特征构成时，它会导致持续的假阴性。我们提出了一种创新的AD方法，利用可解释性将新颖特征捕获为输入空间中无法解释的观测值。通过结合相似性与新颖性的混合方法，我们在多种异常检测基准上实现了强劲性能。该方法在处理多样化异常类型的同时，开创了多个基准的最新水平，并消除了对昂贵背景模型和密集匹配的需求。特别地，我们证明通过考虑新颖特征，在具有挑战性的基准上相比现有最优方法，可将假阴性异常减少高达40%。我们的方法为像素级异常提供了可视觉检查的解释。