DSV: An Alignment Validation Loss for Self-supervised Outlier Model
  Selection
Jaemin Yoo Yue Zhao Lingxiao Zhao Leman Akoglu
Self-supervised learning (SSL) has proven effective in solving various
problems by generating internal supervisory signals. Unsupervised anomaly
detection, which faces the high cost of obtaining true labels, is an area that
can greatly benefit from SSL. However, recent literature suggests that tuning
the hyperparameters (HP) of data augmentation functions is crucial to the
success of SSL-based anomaly detection (SSAD), yet a systematic method for
doing so remains unknown. In this work, we propose DSV (Discordance and
Separability Validation), an unsupervised validation loss to select
high-performing detection models with effective augmentation HPs. DSV captures
the alignment between an augmentation function and the anomaly-generating
mechanism with surrogate losses, which approximate the discordance and
separability of test data, respectively. As a result, the evaluation via DSV
leads to selecting an effective SSAD model exhibiting better alignment, which
results in high detection accuracy. We theoretically derive the degree of
approximation conducted by the surrogate losses and empirically show that DSV
outperforms a wide range of baselines on 21 real-world tasks.