Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer
arxiv(2023)
摘要
Anomaly Detection is challenging as usually only the normal samples are seen
during training and the detector needs to discover anomalies on-the-fly. The
recently proposed deep-learning-based approaches could somehow alleviate the
problem but there is still a long way to go in obtaining an industrial-class
anomaly detector for real-world applications. On the other hand, in some
particular AD tasks, a few anomalous samples are labeled manually for achieving
higher accuracy. However, this performance gain is at the cost of considerable
annotation efforts, which can be intractable in many practical scenarios.
In this work, the above two problems are addressed in a unified framework.
Firstly, inspired by the success of the patch-matching-based AD algorithms, we
train a sliding vision transformer over the residuals generated by a novel
position-constrained patch-matching. Secondly, the conventional pixel-wise
segmentation problem is cast into a block-wise classification problem. Thus the
sliding transformer can attain even higher accuracy with much less annotation
labor. Thirdly, to further reduce the labeling cost, we propose to label the
anomalous regions using only bounding boxes. The unlabeled regions caused by
the weak labels are effectively exploited using a highly-customized
semi-supervised learning scheme equipped with two novel data augmentation
methods. The proposed method outperforms all the state-of-the-art approaches
using all the evaluation metrics in both the unsupervised and supervised
scenarios. On the popular MVTec-AD dataset, our SemiREST algorithm obtains the
Average Precision (AP) of 81.2
supervised anomaly detection. Surprisingly, with the bounding-box-based
semi-supervisions, SemiREST still outperforms the SOTA methods with full
supervision (83.8
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