The goal of Anomaly-Detection (AD) is to identify outliers, or outlying regions, from some unknown distribution given only a set of positive (good) examples. Few-Shot AD (FSAD) aims to solve the same task with a minimal amount of normal examples. Recent embedding-based methods, that compare the embedding vectors of queries to a set of reference embeddings, have demonstrated impressive results for FSAD, where as little as one good example is provided. A different approach, image-reconstruction-based, has been historically used for AD. The idea is to train a model to recover normal images from corrupted observations, assuming that the model will fail to recover regions when encountered with an out-of-distribution image. However, image-reconstruction-based methods were not yet used in the low-shot regime as they need to be trained on a diverse set of normal images in order to properly perform. We suggest using Masked Auto-Encoder (MAE), a self-supervised transformer model trained for recovering missing image regions based on their surroundings for FSAD. We show that MAE performs well by pre-training on an arbitrary set of natural images (ImageNet) and only fine-tuning on a small set of normal images. We name this method MAEDAY. We further find that MAEDAY provides an orthogonal signal to the embedding-based methods and the ensemble of the two approaches achieves very strong SOTA results. We also present a novel task of Zero-Shot AD (ZSAD) where no normal samples are available at training time. We show that MAEDAY performs surprisingly well at this task. Finally, we provide a new dataset for detecting foreign objects on the ground and demonstrate superior results for this task as well. Code is available at https://github.com/EliSchwartz/MAEDAY .
翻译:异常检测( AAD) 的目标是从一些未知的分布区中找出异常点或外围区域, 仅以一组正( 好) 实例为条件, 以某些未知的分布区为目的。 很少的 Shot AD (FSAD) 旨在用最小的普通示例解决相同的任务。 最近的嵌入基方法, 将嵌入的查询矢量与一组参考嵌入的数据集进行比较, 为FSAD展示了令人印象深刻的结果。 我们建议使用马德式自动/ Encoder( MAE), 一个自上而下的新变压式变压器模式, 以从腐败的观测中恢复正常的图像。 设想模型在以发行外观图像时无法恢复正常区域。 然而, 图像重建基于低镜头的系统方法尚未被使用过。 我们用马德- 自动/ Encoder (MAE), 一个自上过的变压变压式变压式变压器, 以其周围的图像区域为最坚固的图像区域。 我们用MAE 正在正常的SMAD RD RD 最终显示该系统 。