论文标题
[可重复性报告]可解释的深一级分类
[Reproducibility Report] Explainable Deep One-Class Classification
论文作者
论文摘要
全卷积数据描述（FCDD），即Hypersphere分类器（HSC）的可解释版本，直接解决图像异常检测（AD）和像素方面的AD，而无需任何事后解释器方法。作者声称，FCDD取得的结果与时尚摄影者和CIFAR-10的样本广告中最新的AD相当，并且超过了MVTEC-AD上像素的最新任务。我们使用作者的代码进行了较小的更改，并提供了运行时要求（CPU内存，GPU内存和训练时间）。我们使用临界差图提出了另一种分析方法，并进一步研究了训练阶段模型的测试性能。
Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods. The authors claim that FCDD achieves results comparable with the state-of-the-art in sample-wise AD on Fashion-MNIST and CIFAR-10 and exceeds the state-of-the-art on the pixel-wise task on MVTec-AD. We reproduced the main results of the paper using the author's code with minor changes and provide runtime requirements to achieve if (CPU memory, GPU memory, and training time). We propose another analysis methodology using a critical difference diagram, and further investigate the test performance of the model during the training phase.
