  Defect detection is a critical research area in artificial intelligence.
Recently, synthetic data-based self-supervised learning has shown great
potential on this task. Although many sophisticated synthesizing strategies
exist, little research has been done to investigate the robustness of models
when faced with different strategies. In this paper, we focus on this issue and
find that existing methods are highly sensitive to them. To alleviate this
issue, we present a Discrepancy Aware Framework (DAF), which demonstrates
robust performance consistently with simple and cheap strategies across
different anomaly detection benchmarks. We hypothesize that the high
sensitivity to synthetic data of existing self-supervised methods arises from
their heavy reliance on the visual appearance of synthetic data during
decoding. In contrast, our method leverages an appearance-agnostic cue to guide
the decoder in identifying defects, thereby alleviating its reliance on
synthetic appearance. To this end, inspired by existing knowledge distillation
methods, we employ a teacher-student network, which is trained based on
synthesized outliers, to compute the discrepancy map as the cue. Extensive
experiments on two challenging datasets prove the robustness of our method.
Under the simple synthesis strategies, it outperforms existing methods by a
large margin. Furthermore, it also achieves the state-of-the-art localization
performance. Code is available at: https://github.com/caiyuxuan1120/DAF.
