Self-supervised learning (SSL) has emerged as a promising paradigm that presents self-generated supervisory signals to real-world problems, bypassing the extensive manual labeling burden. SSL is especially attractive for unsupervised tasks such as anomaly detection, where labeled anomalies are often nonexistent and costly to obtain. While self-supervised anomaly detection (SSAD) has seen a recent surge of interest, the literature has failed to treat data augmentation as a hyperparameter. Meanwhile, recent works have reported that the choice of augmentation has significant impact on detection performance. In this paper, we introduce ST-SSAD (Self-Tuning Self-Supervised Anomaly Detection), the first systematic approach to SSAD in regards to rigorously tuning augmentation. To this end, our work presents two key contributions. The first is a new unsupervised validation loss that quantifies the alignment between the augmented training data and the (unlabeled) test data. In principle we adopt transduction, quantifying the extent to which augmentation mimics the true anomaly-generating mechanism, in contrast to augmenting data with arbitrary pseudo anomalies without regard to test data. Second, we present new differentiable augmentation functions, allowing data augmentation hyperparameter(s) to be tuned end-to-end via our proposed validation loss. Experiments on two testbeds with semantic class anomalies and subtle industrial defects show that systematically tuning augmentation offers significant performance gains over current practices.

翻译：自监督学习（SSL）已成为一种有前景的范式，通过为实际问题提供自生成的监督信号，避免了大量人工标注的负担。SSL尤其适用于异常检测等无监督任务，因为此类任务中标注异常样本往往不存在且获取成本高昂。尽管自监督异常检测（SSAD）近期引发了广泛关注，但现有文献未能将数据增广视为超参数。与此同时，近期研究表明增广策略的选择对检测性能具有显著影响。本文提出ST-SSAD（自调优自监督异常检测），这是首个系统性地针对增广调优的SSAD方法。为此，本研究做出两项关键贡献。第一，我们提出一种新的无监督验证损失，用于量化增广训练数据与（未标注）测试数据之间的对齐程度。原则上采用转导学习，通过量化增广在多大程度上模拟真实异常生成机制，而非像传统方法那样不考虑测试数据而随意生成伪异常。第二，我们提出新的可微分增广函数，使得数据增广超参数能够通过所提出的验证损失进行端到端调优。在包含语义类异常和细微工业缺陷的两个测试平台上的实验表明，系统性增广调优相比当前实践可带来显著的性能提升。