近年來非監督式表徵學習已被證實對於解決挑戰難度較高的異常偵測與分割任務十分有效。本篇論文中，我們提出了一種基於多尺度區塊的表徵學習方法，萃取正常影像中關鍵且具代表性的訊息。通過考慮區域性距離區塊之間的特徵相似度，我們實現了更好的表徵學習。此外，我們改善了預測區塊之間空間組態的自監督學習策略，將其擴展為更加精細的方式，使我們的模型能夠學習更好的區塊之間的幾何關係。通過在影像上滑動不同尺度的區塊，我們的模型從每個區塊中萃取出具代表性的特徵，並將該特徵與正常影像訓練資料集中的特徵做比對，以此來偵測異常區域。我們在公開資料集MVTec AD與BTAD的實驗結果顯示，我們提出的方法在異常偵測與分割的任務中，皆能達到目前為止最好的準確度。

Unsupervised representation learning has been proven to be effective for the challenging anomaly detection/segmentation tasks. In this paper, we propose a multi-scale patch-based representation learning method to extract critical and representative information from normal images. By taking the feature similarity between patches of local distance into account, we can achieve better representation learning. Moreover, we improve the self-supervised learning strategy, i.e., predicting the spatial configuration between the patches, to a more refined manner, thus allowing our model to learn better geometric relationship between the patches. Through sliding patches of different scales all over an image, our model extracts representative features from each patch and compares it with those in the training set of normal images to detect the anomalous regions. Our experimental results on MVTec AD dataset and BTAD dataset demonstrate the proposed method achieves the state-of-the-art accuracy for both anomaly detection and segmentation.

1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Related Works 4
2.1 Reconstruction-based methods . . . . . . . . . . . . . . . . . . . . 4
2.2 Feature-based methods . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Method 7
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.3 Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.3.1 SVDD Loss and Cos Loss . . . . . . . . . . . . . . . . . . 9
3.3.2 Overall Loss . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4 Patch Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.5 Inference and Score Calculation . . . . . . . . . . . . . . . . . . . 13
4 Experiments 15
4.1 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.2 Evaluation Metric . . . . . . . . . . . . . . . . . . . . . . . 16
4.1.3 Implementation Details . . . . . . . . . . . . . . . . . . . . 16
4.2 Experimental Comparison . . . . . . . . . . . . . . . . . . . . . . 17
4.3 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.4 Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.4.1 tSNE Visualization . . . . . . . . . . . . . . . . . . . . . 24
4.4.2 Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 25
4.5 Detailed Analysis and Discussion . . . . . . . . . . . . . . . . . . 26
4.5.1 Time Complexity . . . . . . . . . . . . . . . . . . . . . . . 26
4.5.2 Analysis in Failure Cases . . . . . . . . . . . . . . . . . . . 27
5 Conclusions 32
References 33

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