Deep learning-based methods have achieved a breakthrough in image anomaly
detection, but their complexity introduces a considerable challenge to
understanding why an instance is predicted to be anomalous. We introduce a
novel explanation method that generates multiple counterfactual examples for
each anomaly, capturing diverse concepts of anomalousness. A counterfactual
example is a modification of the anomaly that is perceived as normal by the
anomaly detector. The method provides a high-level semantic explanation of the
mechanism that triggered the anomaly detector, allowing users to explore
"what-if scenarios." Qualitative and quantitative analyses across various image
datasets show that the method applied to state-of-the-art anomaly detectors can
achieve high-quality semantic explanations of detectors.

深度学习方法在图像异常检测方面取得了突破，但其复杂性给了解为何实例被预测为异常带来了相当大的挑战。我们提出了一种新的解释方法，为每个异常生成多个反事实示例，捕捉了异常的不同概念。反事实示例是对异常的修改，被异常检测器视为正常。该方法提供了对触发异常检测器机制的高级语义解释，使用户能够探索 “假设情景”。在各种图像数据集上进行的定性和定量分析表明，该方法应用于最先进的异常检测器可以实现高质量的语义解释。

重塑异常现象：假若异常成为常态？

Reimagining Anomalies: What If Anomalies Were Normal?

Deep neural networks have exhibited remarkable performance across a wide
range of real-world tasks. However, comprehending the underlying reasons for
their effectiveness remains a challenging problem. Interpreting deep neural
networks through examining neurons offers distinct advantages when it comes to
exploring the inner workings of neural networks. Previous research has
indicated that specific neurons within deep vision networks possess semantic
meaning and play pivotal roles in model performance. Nonetheless, the current
methods for generating neuron semantics heavily rely on human intervention,
which hampers their scalability and applicability. To address this limitation,
this paper proposes a novel post-hoc framework for generating semantic
explanations of neurons with large foundation models, without requiring human
intervention or prior knowledge. Our framework is designed to be compatible
with various model architectures and datasets, facilitating automated and
scalable neuron interpretation. Experiments are conducted with both qualitative
and quantitative analysis to verify the effectiveness of our proposed approach.

通过研究神经元来解释深度神经网络并生成语义解释，以解决目前在此方面的限制、缺乏可伸缩性和适用性的问题。