An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
The paper proposes the RD++ approach for anomaly detection by enriching feature compactness and suppressing anomalous signals through a multi-task learning design. For the feature compactness task, RD++ introduces the self-supervised optimal transport method. For the anomalous signal suppression task, RD++ simulates pseudo-abnormal samples with simplex noise and minimizes the reconstruction loss.
RD++ achieves a new state-of-the-art benchmark on the challenging MVTec dataset for both anomaly detection and localization. More importantly, when compared to recent SOTA methods, RD++ runs 6.x times faster than PatchCore and 2.x times faster than CFA, while introducing a negligible latency compared to RD.
What is the motivation for this task?
The paper proposes the RD++ approach for anomaly detection by enriching feature compactness and suppressing anomalous signals through a multi-task learning design. For the feature compactness task, RD++ introduces the self-supervised optimal transport method. For the anomalous signal suppression task, RD++ simulates pseudo-abnormal samples with simplex noise and minimizes the reconstruction loss. RD++ achieves a new state-of-the-art benchmark on the challenging MVTec dataset for both anomaly detection and localization. More importantly, when compared to recent SOTA methods, RD++ runs 6.x times faster than PatchCore and 2.x times faster than CFA, while introducing a negligible latency compared to RD.
Describe the solution you'd like
https://github.com/tientrandinh/Revisiting-Reverse-Distillation/tree/main
Additional context
No response