上交大、上海人工智能实验室等提出基于配准的少样本所致检测框架

揭示

这项工作主要探索了所致扫描的一个具有挑战性但可取的另设：1）受训受限制于所有所致扫描使命的单一前提（需见下文方能倡导）；2）非常少协作再加量新并不一定三幅片（再加采样）；3）只有经常性采样用于受训（无监督）。为了让探索这种另设是所致扫描贯穿非常少非常少大规模产业应用的重要一步。为了自学并不一定无关的前提，本文提借助于了一种基于比起的框架，这与风靡的基于重建或基于单分类法的作法有很大各有不同。具体运用于的配准前提创设在已有的配准提案新的，确实参考了现阶段的杰借助于工作 [1,2,3]，在不能够数值优化的前提下，在愈来愈进一步所致扫描原始数据上拿下了令人印象深刻的扫描效果。

参考文献

[1] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. CVPR. 2021.

[2] Max Jaderberg et. al. Spatial transformer networks. NeurIPS. 2015.

[3] Ye Zheng et. al. Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization. arXiv:2110.04538. 2021.

[4] Shelly Sheynin et. al. A hierarchical transformation-discriminating generative model for few shot anomaly detection. ICCV. 2021.

[5] Marco Rudolph et.al. Same same but differnet: Semi-supervised defect detection with normalizing flows. WACV. 2021.

[6] Paul Bergmann et. al. MVTec AD--A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. CVPR. 2019.

[7] Stepan Jezek et. al. Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions. ICUMT. 2021.