Cross-Scale Denoising Reverse Distillation for Anomaly Detection

Effective discrepancy representation of anomalies plays a crucial role in visual anomaly detection. Recent advances build upon reverse distillation paradigm that boost the teacher–student model’s discrimination capability on anomalies; however, they are still susceptible to the size variation of unpredictable anomalies. To generalize the anomaly size variation, we propose a new algorithm cross-scale denoising reverse distillation (CDRD), which integrates cross-scale denoising with reverse distillation to exchange multiscale perception and enhance the fine-grained representation of features. Specifically, we introduce a cross-scale anomalous signal suppression procedure in the teacher network to facilitate the interaction of information across different scales, thereby enabling the student network to learn more robust normal data representations. In the knowledge transfer process, a fusion compression module acts as an intermediate transmitter of information, aiming to obtain a compact embedding while abandoning anomaly perturbations. Moreover, we construct a detail supplement module in the student network to prevent the loss of key information in the deconvolution process of the decoder. Experiments on well-known datasets demonstrate that our CDRD brings significant improvements over the next best competitor.