  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.
