Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Authors: Guoyang Xie
Jinbao Wang
Jiaqi Liu
Yaochu Jin
Feng Zheng
Venue: ICLR 2023 poster (2023)

Abstract: In the area of few-shot anomaly detection (FSAD), efficient visual feature plays an essential role in the memory bank $\mathcal{M}$-based methods. However, these methods do not account for the relationship between the visual feature and its rotated visual feature, drastically limiting the anomaly detection performance. To push the limits, we reveal that rotation-invariant feature property has a significant impact on industrial-based FSAD. Specifically, we utilize graph representation in FSAD and provide a novel visual isometric invariant feature (VIIF) as an anomaly measurement feature. As a result, VIIF can robustly improve the anomaly discriminating ability and can further reduce the size of redundant features stored in $\mathcal{M}$ by a large amount. Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and improve the performance of anomaly detection. A comprehensive evaluation is provided for comparing GraphCore and other SOTA anomaly detection models under our proposed few-shot anomaly detection setting, which shows GraphCore can increase average AUC by 5.8%, 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%, 22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively.

Published as a conference paper at ICLR 2023
PUSHING THE LIMITS OF FEW-SHOT ANOMALY DE- TECTION IN INDUSTRY VISION: GRAPHCORE
Guoyang Xie 2. Jingbao Wang Jiaqi Liu Feng Zheng Yaochu Jin
'Research Institute of Trustworthy Autonomous Systems, Southern University Science and Technology. Shenzhen 518055, China 2NICE Group, University of Surrey, Guildford, GU2 7YX, United Kingdom "NICE Group, Bielefeld University, Bielefeld 33619, Germany guoyang wieesurrey.ee..eeeeeo.....reeeu ac..reyy.cccuu. ac. linkingringginin.. 1iujq320211mmaii1 sustech. edu.. cn, zhengf@sustech. edu. cn yaochu.
com
ABSTRACT
In the area of few-shot anomaly detection (FSAD), efficient visual feature plays an essential role in the memory bank M--bsedd methods. However, these meth- ods do not account for the relationship between the visual feature and its rotated visual feature, drastically limiting the anomaly detection performance. To push the limits, we reveal that rotation-invariant feature property has significant im- pact on industrial-basee FSAD. Specifically, we utilize graph representation in FSAD and provide novel visual isometric invariant feature (VIIF) as an anomaly measurement feature. As result, VIIF can robustly improve the anomaly dis- criminating ability and can further reduce the size of redundant features stored in .M by large amount. Besides, provide novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and improve the per- formance of anomaly detection. A comprehensive evaluation is provided for com- paring GraphCore and other SOTA anomaly detection models under our proposed few-shot anomaly detection setting, which shows GraphCore age AUC by 5.8%. 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%, 22.0%, 16.9% and 14. % on MPDD for 4, and 8-shot cases, respectively.
increase
INTRODUCTION
With the rapid development of deep vision detection technology in artificial intelligence, detect- ing anomalies/defects the surface of industrial products has received unprecedented attention. Changeover in manufacturing refers to converting a line or machine from processing one product to another. Since the equipment has not been completely fine-tuned after the start of the production line, changeover frequently results in unsatisfactory anomaly detection (AD) performance.
How achieve rapid training of industrial product models in the changeover scenario while assuring accurate anomaly detection is a critical issue in the actual production process. The current state of AD in the industry as follows: (1) In terms of detection accuracy, the performance of state-of- the-art (SOTA) AD models degrades dramatically during the changeover. Current mainstream work utilizes considerable amount of training data as input to train the model, as shown in Fig. I(a). However, this will make data collecting challenging, even for unsupervised learning. As result, many approaches based on few-shot learning at the price of accuracy have been proposed. For instance, Huang et al. (2022) employ meta-learning, settings, impossible to migrate to the new product during the changeover flexibly, and the detection accuracy cannot be guaranteed.
shown in Fig. 1(b). While due to complicated
(2) In terms of training speed, when large amount of data is utilized for training. the training progress for new goods is slowed in the actual production line. As is well-known, vanilla unsupervised AD requires to collect large amount of information.
Even though meta-learning works in few-shot learning, as shown in Fig. 1(b), it is still necessary to train massive portion of previously collected data.
"Contributed Equally, Corresponding Authors.

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Published as a conference paper at ICLR 2023
Toinnn
Thininghow
Thiiiing
Nauarre Normal
Szzsples
Maskira Niemal
Nomel Samplon
Sunques
Imageblet
Imagthiet
Madel
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Test Set
Support Set
Test Set
(4) Unuagervied cemuly Detection (Vazilaa)
(b) Fow-dostlsseeevsed Ansmaaly Detecton
(6) Fiwwsbatt Unsuperciad Anomally Detactio
(Mfeta Heaming)
(Dus))
Figure 1: Different from (a) vanilla unsupervised AD and (b) few-shot unsupervised AD in meta learning. As input training samples, our setting (c) only utilizes a small number of normal samples. For our setting (c), there is requirement to aggregate training categories in advance. The proposed model, vision isometric invariant GNN, can fast obtain the invariant feature within few normal samples, and its accuracy outperforms models trained meta-learning context.
We state that AD industrial products requires just small quantity of data to achieve performance comparable to a large amount of data, i.e., small quantity of image data can contain sufficient information to represent large number of data. Due to the fact that industrial products are manu- factured with high stability (no evident distortion of shape and color cast), the taken images lack the diversity of natural images, and there is a problem with the shooting angle or rotation. Therefore, it is essential to extract rotation-invariant structural features. As graph neural networks (GNNs) are capable of robustly extracting non-serialized structural features (Han et al. (2022), [runa et al. (2013), |Hmmiltn al. (2017). Xu et al. (2018)), and they integrate global information better and faster Wang et al. 22220 : Li et al. (2020). : They are more suited than convolution neural networks (CNNs) to handle the problem of extracting rotation-invariant features. For this reason, the core idea of the proposed CraphCore method in this paper is to use the visual isometric invariant features (VIIFs) as the anomaly measurement features. In the method using memory bank (M) the AD paradigm, PatchCore (Roth et al. (2022)) uses ResNet (He et al. (2016)) as the feature extractor. However, since their features obtained by CNNs do not have rotation invariance (Dieleman et al.) (016)), large number of redundant features are stored in M. Note that these redundant features maybe come from multiple rotation features of the same patch structure. will hence require a huge quantity of training data to ensure the high accuracy of the test set. To avoid these redundant fea- tures, we propose VIIFs, which not only produce more robust visual features but also dramatically lower the size of M and accelerate detection.
Based on the previous considerations, the goal of our work is to handle the cold start the produc- tion line during the changeover. As shown in Fig. 1(c), new FSAD method, called GraphCore, is developed that employs small number of normal samples to accomplish fast training and compet- itive AD accuracy performance of the new product. On the one hand, by utilizing small amount of data, we would rapidly train and accelerate the speed of anomaly inference. On the other hand, because we directly train new product samples, adaptation and migration of anomalies from the old product to the new product do not occur.
Contributions. In summary, the main contributions of this work are follows:
We present feamere-augmented method for FSAD in order to investigate the property of visual features generated by CNNs.
We propose novel anomaly detection model, GraphCore, to add VIIF into the memory bank-based AD paradigm, which can drastically reduce the quantity of redundant visual features.
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The experimental results show that the proposed VIIFs effective and can significantly enhance the FSAD performance on MVTec AD and MPDD.
Related Work. Few-shot anomaly detection (FSAD) is an attractive research topic. However, there are only few papers devoted to the industrial image FSAD. Some works (Liznerski et al. (2020); Pang et al. (202T); ) Ding et al. (2022) experiment with few-shot abnormal images in the test set, which contradicts our assumptions that abnormal images existed. While others (Wu et al. (2021); Huang et al. 2022)) conduct experiments in meta-learning setting. This configuration has the disadvantage of requiring high number of base class images and being incapable of addressing the shortage of data under cold-start conditions in industrial applications. PatchCore (Roth et al. (2022), ) SPADE (Cohen & Hoshen (020)), and PaDiM (Defard al.] (202I)) investigated AD performance on MVTec AD few-shot setting. However, these approaches are not intended for changeover-based few-shot settings. Thus their performance cannot satisfy the requirements of manufacturing changeover. In this research, we propose a feature augmentation method for FSAD that can rapidly finish the training anomaly detection models with small quantity of data and meet manufacturing changeover requirements.
2 APPROACH
Problem Setting, Fig. (e) outlines the formal definition of the problem setting for the proposed FSAD. Given training set of only n normal samples during training, where I 8, from a specific category. At test time, given a normal or abnormal sample from target category, the anomaly detection model should predict whether or not the image is anomalous and localize the anomaly region if the prediction result is anomalous.
Challenges. For the FSAD proposed in Fig. [c.. we attempt to detect anomalies in the test sample using only small number of normal images the training dataset. The key challenges consist of: (1) Each category's 's training dataset contains only normal samples, i.e., no annotations at the image pixel level. (2) There few normal samples of the training set available. In our proposed setting, there are fewer than 8 training samples.
Motivation. In the realistic industrial image dataset (Bergmann et al. (2019) Jezek et al. (222
the images under certain categories are extremely similar. Most of them can be converted to one another with simple data augmentation, such the meta (Fig. 2 and the screw (Fig. 6p. For instance, rotation augmentation can effectively provide a new screw dataset. Consequently, when faced with the challenges stated in Section 2 our natural inclination is to acquire additional data through data augmentation. Then, the feature memory bank (Fig. 4 can store more useful features.
ImoggNett Pretrinnol
Optional
Subsmpling
Testing
Memoryy Bank
Augmentaioon
(eg,
0.9267 Anomaly Scoce
Neares Neighhor
Festure
ImageNet Pestrained Model
Anomaly Mask
Figure 2: Augmentationn-PachCCrr Architecture
2.1 AUGMENNTATION+PATCHCOO
To validate our insight, we have adapted PatchCore (Roth et al. (2022) to our model. We denote augmentation (rotation) with PatchCore (R). The architecture depicted in detail in Fig. 2 Before extracting features from the ImageNet pre-trained model, we augment the data (c.g.. by rotating the data).
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Algorithm 1: Aug-(R) memory bank
Input ImageNet pre-trained Φ, all normal samples Xxx, data augmentation operator a, patch fcature extractor P. memory size target random linear projection 4b.
Output: Patch-level augmented memory bank M.
M < 0:
for 2, Xx do
z + o(z,);
M + P()(x;)):
M + P()(z?))
end for
Mc f- [] //Apply coreset sampling for memory bank do
for i E ..... ,0,....,
arg max min [Iv(m)) +1())l22
mEMMMM
10
Mc t- Mc U (ma))
end for
12 f- Mc.
In the training phase, the aim of the training phase is build up a memory bank, which stores the neighborhood-aware features from all normal samples. At test time, the test image is predicted as anomalies if least one patch is anomalous, and pixel-level anomaly segmentation is computed via the score of each patch feature. The feature memory construction method is shown in Algorithm [ We default set ResNet18 (He et (2016)) as the feature extraction model. Conceptually, coreset sampling (Sener & Savarese (201 8)) for memory bank aims to balance the size of the memory bank with the performance of anomaly detection. And the size of the memory bank has considerable
impact the inference speed. In Section 3.3 we discuss the effect of the sampling rate in detail.
In testing phase, with the normal patches feature bank M, the image-level anomaly score s for the test image fest is computed by the maximum score s* between the test image's 's patch feature P(a and its respective nearest neighbour m" in M.
From Table 2 and Table we easily observe that the performance of (u) greatly outper- forms the SOTA models under the proposed few-shot setting.
2.2 VISION ISOMETRIC INVARIANT FEATURE
In Section 2.1 we heuristically demonstrate that Augmentationn+Patchorrr outperforms SOTA models in the few-shot anomaly detection context proposed. Essentially, the data augmentation method immediately incorporates the features of normal samples into the memory bank. In other words, Augmentation+Patthhrrr improves the probability of locating a subset feature, allowing the anomaly score of the test image be caleulated with greater precision. Therefore, whether possible to extract the invariant representational features from a small number of nor- mal samples and add them the feature memory bank. As demonstrated in Fig. 3 propose a new model for feature extraction: vision isometric invariant graph neural network (VIIG). The proposed model is motivated by Section 2 and tries to extract the visual isometric invariant feature (VIIF) from each patch of the normal sample. As previously stated, the majority of industrial vi- sual anomaly detection datasets are transformable via rotation, translation, and flipping. Thus, the isomorphism of GNN suited industrial visual anomaly detection excellently.
question
Feature
ImageNet Pretmined Model
Invariant Festure
Vision Isometric Invariant GNN
Figure 3: Convolution feature VS vision isometric invariant feature.
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Graph
Soquenee Structuee
Asgropate Fosur Neighbour
Graph Sampling
Figure 4: Vision isometric invariant GNN pipeline.
2.3 GRAPH REPRESENTATION OF IMAGE
Fig. 4 shows the feature extraction process of GraphCore. Specifically, for a normal sample image with size of H W x 3, evenly separate it an NN patch. In addition, each patch is transformed into a feature vector fi E D. So we have the features F = [fi, f2. fNN]. where D is the feature dimension and 1, 2, ... .N. We view these features unordered nodes V {vi, U2," "N ) For certain each node u, we denote the K nearest neighbours . ((vx) and add an edge ei directed from Vj o; for all ,/ (w;).. Hence, each patch of normal samples can be denoted graph g = (V, & refers all the edges of Graph G.
2.4
GRAPH FEATURE PROCESSING
Fig. shows the architecture of the proposed vision isometric invariant GNN. To be specifie, set the feature extraction as GCN (Kipf & Welling (2017)). We aggregate features for each node by exchanging information with its neighbour nodes. In specific, the feature extraction operates as follows:
(1) where W Waggregate and W Wupdatte denote the weights of the aggregation and update operations. Both of them can be optimized in an end-to-end manner. Specifically, the aggregation operation for each node calculated by aggregating neighbouring nodes' features:
G' = F W)
W (oggregate), W Wupdate);
f h(fo 9(f,, W Waggregate); W Wupdate);
where is the node feature update function and g is the node feature aggregate feature function. N(f)) denotes the set of neighbor nodes of f at the 1-th layer. Specifically, employ max-relative graph convolution (Li et al. (019)) as our operator. So and h are defined as:
g(-) = ft max - fyli ((x:)).
(3)
h(-) = f/ = W Wapdate.
In Equations 3 and g(-) is max-pooling vertex feature aggregator that aggregates the difference in features between node it and all of its neighbours. h(-) is MLP layer with batch normalization and ReLU activation.
Vision loometic Inserient GNN
Invaiian Feature
Corrset Subsampling
Training
Testng
Memory
Graph Trunsformassin
Neaeet Neighbor Search
Anomaly
Vision Esemetric lavarian: GNN
Anomaly Mask
Figure 5: Vision isometrie invariant GNN for FSAD.

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Published as a conference paper at ICLR 2023
M
Aggmentation
(eg.. Rosation
Festure
Feetaae
Sampling
M
Viston Isometric Invaciant GNN
M
Invaciant Feature
A Sampling
Input Normal Samples
(a) Vanilla Anomaly Detection
(b) Few-she shot Anomaly Detection (Ours)
Figure Vision GNN architecture. (a) vanilla AD and (b) our proposed FSAD.
2.5 GRAPHCORE ARCHITECTURE
Fig. 5 shows the whole architecture GraphCore. the training phase, the most significant dif- ference between GraphCore and Augmentation-PPachhorrr is the feature memory bank construction algorithm. The feature construction algorithm is the same as (ug.(R) memory bank in Algorithm [
Note that we use vision isometric invariant GNN feature extractor P without data augmentation. In the testing phase, the computation of anomaly score se for GraphCore is highly similar to the one in Tugmentationn-PathhC... patch sample. The architecture details of the GraphCore are shown in the reference Table 21
The only difference is the feature extraction method for each normal
2.6 A UNIFIED VIEW OF AUGMENTATION-PATTHHHR
AND GRAPHCORE
and GraphCore. Augmenta- tion+PatchCore prompts GraphCore to obtain the isometric invariant feature. Therefore, GraphCore can improve the probability of locating feature subset, allowing the anomaly score of test im- age to be calculated most precisely and rapidly. Table T shows the difference between PatchCore, Augmentatioon-PaathCCoo and GraphCore in terms of architectural details.
Fig. 6 depicts a unified view of both Augmentationn
Table 1: Unified view for three methods. Network
Augmentation
Model
No
ImageNet Pre-trained Model ImageNet Pre-trained Model
PatchCore
Rotation
ARg
No
GNN
GraphCore
EXPERIMENT
3.1 EXPERIMENT SETTING
Datasets. To demonstrate the generalization of our proposed method, we conduct experiments on three datasets, namely MVTec AD (Bergmann et al. (2019)), MPDD (Jezek al. (202T) and MVTec LOCO AD (Bergmann al. )2022
Competing Methods. RegAD (Huang et al. (2022) is the SOTA FSAD method. It works under a meta-learning setting: aggregated training multiple categories and adapting to unseen categories, using few-shot unseen images as a support set. However, our proposed few-shot setting utilizes only few images training set and not several categories. Taking into account the fairness of the periments, we reimplement the classical and SOTA approaches in the field unsupervised anomaly detection, such as SPADE (Cohen & Hoshen (202)), STPM (Wang et al. (2021a)), RD4AD (Deng

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Published as a conference paper at ICLR 2023
60-
HUnOC Grasicers
AJROC
Image AUROC @ Corp
Imuge Au10C Graghcers
Mae A ant segon
Irage RagAo
truge HURO: hoplo
Number Shot
(a) MPDD
(b) MVToc AD
Figure 7: GraphCore VS AngmennationnPPachhoorr VS RegAD on various numbers of shot (K).
& Li (2022)) CFA (Lee et al. (2022) ) and PatchCore (Roth et al. (2022)) ) using the official source code for comparison under few-shot setting. PatchCore-1 is the result of our reimplementation with a 1 % sampling rate, PatchCore- 10 and PatchCore-22 are the results at 10% and 25% sampling
rates, respectively, and RegAD-L is the RegAD experiment with our few-shot setting.
3.2 COMPARISON WITH THE SOTA METHOSS
The comparative findings between MVTec and MPDD are shown in Table 2 Especially the per- formance of RegAD under the meta-learning setting also listed the table. In comparison to SOTA models, GraphCore improves average AUC by 5,8%, 4.1%. 3,4%, and 1.6% on MVTee and by 25.5%, 22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively. From Fig. 7 it can be easily observed that GraphCore significantly outperforms the SOTA approach at the image and pixel level from 1-shot to 8-shot. As can be seen, the performance of GraphCore and Augmentation+Paute PaachCorre surpasses the other methods when using only few samples for training.
Table 2: FSAD average results for all categories on MVTec AD and MPDD. The sampling ratio 0.01, xly represents image AUROC and pixel AUROC. The results for PaDiM, PatchCore- 10 and PatchCore-25 are reported from Roth et al. (2022). The results for RegAD-L and RegAD are
reported from Huang et al. (2022).
The best-performing method is in bold.
Dateset
K
AngrRii
GraphCaree
CPA
SPADE
PaDiM
STPM
RDMAD
PatchCore-1
PanelCorr-10
PatchCore-25
RegAD-L
RegAD
87.4/445
89.9196.6
73.5/007
60.979.1
76.1/882
69.75582
74.459.0
76.590.1
B4.1|22
82.4-
S1.1|910
78,900.5
7422999
753/71.8
B64/9..1
87.2333
85.79446
92.2/9500
92.997.4
33.0913
716/801
743/003
769/722
89.595.0
84.]
8
95.4/66.4
95.999.
90.99166
75.3|80.5
7746516
78.5/300
04.393.6
60.4
91.29667
81.9/94.
84.7/8.2
S8.(1777
57./739
94.117.11
S6.17332
80.2778.5
$1.5--
61.4)4455
$0.6/6.2
50.8|-
634/91.2
94.9/95.2
85.795.7
50.3/78.7
59.37559
62.67622
62.1)755
39,877588
54.2
68.89339
85.1998.
86.0/95.
00.0/790
58.9766
63. 63.1]666
600090.3
61 4|-
21.9/9511
Considering that RegAD only shows detailed results of various categories above 2-shot, we only show the detailed results of 2-shot in the main text, and the results of 1-shot, 4-shot, and 8-shot in the appendix. As shown in Table 3 GraphCore outperforms all other baseline methods in 12 out of the 15 categories at the image level and outperforms all other baselines in 11 out of the 15 categories at the pixel level on MVTec AD. Moreover, results Table 4 show that GraphCore outperforms all other baselines in 5 out of the 6 categories at the image level and outperforms all other baselines in all categories at the pixel level on MPDD.
3.3 ABLATION STUDIES
Sampling Rate. When demonstrated in Fig. 8. our technique significantly improves the sample rate increases from 0.0001 to 0.001, after which the increase in sampling rate has a flattening effect on the performance gain. In other words, as the sampling rate steadily increases, the performance of GraphCore is insensitive to the sampling rate.
Nearest Neighbour. In Fig. 8 the green colour represents the performance of GraphCore's 's nearest neighbour search, and the blue colour represents the performance of GraphCore's 's nearest neigh- bour search. As can be seen, increasing the number of neighbours from 3 to 9 greatly increases
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Published as a conference paper at ICLR 2023
Table FSAD results on MVTèc AD. The number shots K and the sampling ratio 0.01, x represents image AUROC and pixel AUROC. The results for PaDiM. PatchCore-IV 10 and PatchCore- 25 are reported from Roth et al. (2022). The results for RegAD are reported from Huang et al.
(022).
The best- pesst-eeforming method is in bold.
Calegory
Aug./Ri
GraphiCore
SPADE
PaleM
RI4AD
PutchCore-1
PatchCore-10
PatchCore-25
RegAD
Botte
99.7/98.5
99.8 99.8
93. 23.75
95.7/86.8
93.8:84.6
91.2
99.798.5
99,498.0
94.7/96.2
65.16654
Capulle
66.5,97.7
73.2997.
53.4/85.9
48.7
45.2199.2
50.5/78.2
67.2/97.7
67.5,97.3
Capes
99.6
92.8/74.
96.5,98.9
Grid
75.7/998
81.5 80.6
40441114
75.8/75.9
72.0/61.2
75.2
61.7,67.5
84,0 77.4
Hazelout
99.5
00.3/74.5
93.4/64.8
Leather
99.3
100 100.9944
100
577 97..9/89.2
75.8/7522
96.7/86.5
100,99.3
49.4
Pul
87.893.9
88.67441
67.4/91.5
64..2/57.2
48.7
62.8/70.22
87.4
31.3
Screw
63,6/960
65.7
58.7
51.3/70.2
51,9/51.8
54.3160.3
98.3
52./94,4
Tile
190
10096.8
99.9/11.5
90..2/82.3
91,4/58.2
89.9/59.2
100/9601
94.3 94.3
Toothbrush
83.6/98.2
87.3
86.9/93.9
80.2/76.8
76.5/66.3
77.1/78.33
83,9/98.2
98.2
Transistor
96.3/94.1
97.1 99.2
72.5/80.3
51.8/73.6
82.4/47.5
78.1/67.88
96.9.95.0
85.0993.4
Wood
97.1/98.4
97.5 99.5
2922
30.3
95.8/48.4
93.7193.8
97.2993.0
99.2933.5
Zipper
96.9/99.0
97.5.99.3
50.5|96.1
49.4/93.7
47,6,56.3
49.5/51.2
95.3998.2
86.3/95.1
90.4/6.3
91.9 96.9
81.1/9100
70.7179.9
78.5/90.5
74.2,99.8
75.3/71.8
87,8
36,49331
$7.2/93.3
85.7/94.6
Table 4: FSAD results MPDD. The number of shots K is and the sampling ratio is 0.01, x represents image AUROC and pixel AUROC. The results for PaDiM, PatchCore-10 10 and PatchCore- 25 are reported from Roth et al. (2022). The results for RegAD are reported from Huang et al. (022)). The bert-perf method is in bold.
Category
(ug.
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bracket Black
66.8/92.1
67.0 92.5
54.3/75.8
62.4
94.5/75.1
91.7|75.4
58.6 78.9
63.3/-
Bracket Brown
76.1/91.9
77.2 92.6
66.8|77.5
59.5/71.9
62.3/73.2
58.8/73.4
70.7 76.9
59.4|-
Bracket White
87.2/97.1
89.4 97.5
68.7|70.8
67.2/72.4
53,8/64,2
55.6/62.4
70.4,68.1
55.6|-
Connector
98.6/97.2
98.9 97.7
58.5|88.2
59.2
51.6/83.4
53.7
59.2
73.0|-
Metal Plate
99,9/98.4
99.9 99.1
62.7|84.3
64.2/75.9
62.4(83.2
65.2/76.5
86.3
61.7|-
Tubes 79.2|92.6
79.8 93.1
40.7|72.8
35.6
49.6/75.6
45.9(77.1
79.5
67.1|-
Average
84.6/94.9
85.4 95.4
58.6/78.2
58.0/75.4
62.4/75.8
61.8/74.5
59.6 79.2
63.4/93.2
performance at the pixel level when the sampling rate is low, but does not enhance performance at the image level. As the sampling rate increases, the gain of the number of pixels' neighbours approaches zero.
Augmentation Methods. Fig. demonstrates that the performance of PatchCore on MVTec AD and MPDD is relatively weak, but (R) demonstrates higher performance. It demonstrates heuristically that enhancement to feature rotation is significantly effective. Moreover, Graph- Core outperforms (R) by a large margin, confirming our assumption that GraphCore can extract the isometric invariant feature from industrial-based anomaly detection images.
1.3
10
3.0
0.5
Parl susei (hal)
staci (aol)
ALROE Bacl
AUTOC Med)
AUnOC
AUROC (be]]
Image AUDOC Hedd)
Imopo AUECC (oed)
mage AURCC 24-3)
6-001
Sempling
Sampligg
(a) MPDD
(b) MVTec AD
(c) MVTee LOCO AD
Figure Ablation results on sampling rates and the number of nearest neighbors.
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Published as a conference paper at ICLR 2023
60-
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Shot
(a) MPDD
(b) MVToc AD
Figure 9: GraphCore vs Augmenttaioon-Putchoorr vs PatchCore on various number of shot (K).
MVTec AD
MPDD
Figure 10: Visualization results of the proposed method on MVTec AD and MPDD. The first row denotes the training example in the 1-shot setting. The second row is test samples (abnormal). The third row is the heatmap on test samples. The fourth row is the anomaly mask (ground truth).
3.4 VISUALIZATION
Fig. 10 shows the visualization results obtained by our method on MVTec AD and MPDD with sampling rates of 0.01 and 1 shot, respectively. Each column represents different item type, and the four rows, from top to bottom, are the detection image, anomaly score map, anomaly map on detection image, and ground truth. According to the results, our method can produce a satisfactory impact of anomaly localization on various objects, indicating that it has a strong generalized ability even the 1-shot case.
CONCLUSION
In this study, we introduce new approach, GraphCore, for industrial-based few-shot visual anomaly detection. Initially, by investigating the CNN- -eNn-ggneeteed feature space, we present a simple pipeline Augmentation++Pacchoonn for obtaining rotation-invarian features. It turns out that this simple baseline can significantly improve anomaly detection performance. to capture the isometric invariant features of normal industrial samples. It outperforms the SOTA models by a large margin using only a few normal samples (S 8) for training. The majority of industrial datasets for anomaly detection possess isomorphism, which is property idcally suited to GraphCore. We will continue to push the limits of industrial based few-shot anomaly detection in the future.
further propose GraphCore

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Published as a conference paper at ICLR 2023
5 ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China under Grant No. 62122035, 62206 122, and 61972188. Y. Jin supported by an Alexander von Humboldt Professor- ship for Al endowed by the German Federal Ministry of Education and Research.
REFERENCES
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Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger. Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization. Interna- tional Journal of Computer Vision, 130(4):947-969, 2022.
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APPENDIX
6.1 DATASET
MVTec AD is the most popular dataset for industrial image anomaly detection (Bergmann et al. 2019)), which consists of 15 categories of items, including a total of 3629 normal images as a training and collection of 1725 normal images and abnormal images test set. All images have a resolution between 700x 700 and 1024x 1024 pixels.
MPDD is more challenging AD dataset containing 6 classes of metal parts (Jezek et al. (221) The images are taken in different spatial directions and distances and under the condition of non- uniform background, so more challenging. The training contains 888 normal images, and the test set contains 176 normal images and 282 abnormal images. The resolution of all images is 1024x 1024 pixels.
MVTEC LOCO AD adds logical abnormal images outside the structural class abnormal image (Bergmann et al. (2022)). The dataset contains 1,772 normal images a training set, and 304 normal images are used as validation set. The test set contains 575 normal images, 432 structural abnormal images, and 561 logic abnormal images. Due to the different calculation methods of logic abnormal detection metric, retaining the remaining 575 normal images and 432 structural abnormal images as a test set for experiments. Each image is 850 to 1600 pixels in height and 800 to 1700 pixels wide.
abandon the logical abnormal image of the test concentration,
6.2 EXPERIMENT RESULTS
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Published as a conference paper at ICLR 2023
Table 5: Results of anomaly detection. Setting: New Few-shot Setting, K (number of shot)=1,
Dataset: MVTec, Sampling Ratio: 0.01, Metries: Image AUROC. The number shot for RegAD
is 2. The data for PaDiM and PatchCore-10 10, PatchCore-25 are from Roth et al. 2022).
Calegory
(ug(R)
GraphCore
CBA
SPADE
PaDiM
STPM
RD4AD
PatchCure-1
PatchCore-D 10 PatchCure-25
RegAD
Bottle
99.7
99.8
96.7
93.2
93.2
91.2
96.5
Cable
90.1
91.1
65.4
60.1
59.8
58.3
65.5
Capsule
64.7
72.1
50.2
45.6
43.2
44,7
49.8
Carpet
99.3
99.3
97.1
93.2
90.5
92.5
97.2
Grid
70.8
80.9
79.2
75.1
71.2
74.3
78.9
Hazelnut
97.4
98.5
98.1
95.0
90.3
93.2
98.0
Leather
100
97.2
95.1
96,5
100
Meta
77.0
92.5
66.1
60.2
58.2
63.4
65.6
Pill
81.0
81.2
66.3
59.7
57.3
62.4
65.1
Screw
55.9
49.6
51.2
53.5
54.8
Tile
99.9
99.2
99.8
89.5
90.2
88.7
99.5
Toothbrush
84.4
85.2
86.7
78.5
75.2
77.8
85.8
Transistor
71.5
83.2
77.5
Wood
97.0
97.3
98.1
49.5
95.4
93.5
98.9
Zipper
97.4
97.5
50.3
48.5
45.2
48.6
51.2
Average
87.4
89.9
78.75
69.83
74.4
78.5
83.40
84.10
82.4
Table 6: Setting: Ours Few-shot Setting, K (number of shot)=1, Dataset: MVTec, Sampling Ra-
tio: 0.01, Metrics: Pixel AUROC. The number of shot for RegAD is 2. The data for PaDiM and
PatchCore- 10, PatchCore-22 25 are from Roth et al. (2022).
Category
Ang(R)
GraphCore
CBA
SPADK
PallM
STPM
RIHAD
PatehCore-1
PatchCore-10
PatchCore-25
RegAD
Bottl
99.8
93.2
83,2
84.3
81.7
93.0
Cable
95.1
96.2
88.2
78.2
50.9
64.8
87.0
Capsule
85.6
79.2
49.2
77.9
87.7
Carpet
99.3
97.5
95.2
60.5
72.5
98.8
Grid
70.5
76.9
81.3
75.6
61.2
74.3
84.1
Hazelnut
97.1
98.5
98.1
88.2
73.3
63.2
97.5
Leather
99.3
99.5
99.2
88.3
75.1
86.5
99.2
Meta Nut
93.2
92.5
89.5
58.5
51.1
68.7
90.1
95.7
96.2
91.2
54.2
49,3
65.6
90.4
Screw
92.0
93.4
96.5
69.6
51.2
59.7
95.8
Tile
95.8
96.8
81.5
81.5
57.2
88.7
82.7
93.8
77.8
Transistor
93.6
96.2
79.5
73,5
43.2
77.5
78.8
Wood
93.1
94.3
91.8
89.5
45.4
93.5
90.7
Zipper
98.5
93.2
55.2
48.6
Average
94.47
95.60
90.67
79.07
88.20
58.15
69.03
90.85
92.00
92.40
Table 7: Setting: Ours Few-shot Setting, K (number of shot)=2, Dataset: MV'Te,, Sampling Ratio:
0.01, Metrics: Image AUROC. The data for PaDiM and PatchCore- 10, PatchCore-25 are from Roth
al. (2022).
Category
Aug(R)
GraphCore
RegAD
Bottle
99.7
99.8
93.7
95.7
93.8
91.2
99.7
99.4
Cable
94.7
95.2
89.3
60.4
60.2
65.3
94.9
65.1
Capsule
66.5
73.2
53.4
48.7
45.2
50.5
67.2
67.5
Carpet
99.4
99.4
97.6
92.1
90.8
92.8
99.1
96.5
Grid
80.4
75.8
72.6
75.2
61.7
Huzelnut
99.7
99.5
99.4
95.2
90.3
93.4
93.5
96.0
Leather
100
100)
100
97.9
95.8
96.7
100
99.4
59.4
63.4
Pill
87.8
88.6
67.4
60.2
58.7
62.8
87.4
81.3
Screw
63.6
65.7
58.2
51.3
51.9
$4.3
48.3
52.5
Tile
88.9
100
Toothbrush
83.6
87.3
86.9
80.2
76.5
77.1
83.9
86.6
Transistor
96.3
97.1
72.5
51.6
82.4
78.1
96.9
$6.0
Wood
95.8
Zipper
96.9
97.5
$0.5
49.4
47.6
49,5
95.3
86.3
Average
90.40
91.91
81.06
70.69
78.90
74.16
75.53
87.81
86.40
87.20
85.70
12

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Published as a conference paper at ICLR 2023
Table 8: Setting: Ours Few-shot Setting, K (number of shot)=2, Dataset: MVTec, Sampling Ratio:
0.01, Metrics: Pixel AUROC. The data for PaDiM and PatchCore- 10, PatchCore 25 are from Roth
et al. (2022).
Calegory
Aug(R)
GraphCore
CBA
SPADK
PaiiM
STPM
RI4AD
PatehCore-1
PatchCore-10
PatchCore-25
RegAl
Bottle
91.5
84.6
81.7
98.5
Cable
96.2
96.3
88.9
78.6
51.6
65.4
97.8
91.7
Capsule
97.7
97.8
85.9
79.8
59.2
78.2
97.7
97.3
Carpet
99.6
97.9
95.6
60.5
74.2
99.0
Grid
79.8
80.6
81.4
75,9
61.2
76.3
67.5
77.4
Hazelnut
97.9
98.2
98.2
88.9
74.5
64.8
96.4
98.
Leather
99.3
99.4
99.3
89.2
75.2
86.5
99.3
98.0
Meta Nut
96.8
98.1
89.7
59.5
51.1
68.9
97.1
96.9
94.1
91.5
57.2
49,9
70.2
96.8
Screw
96.0
96.5
96.7
70.2
51.8
60.8
90.8
94.4
Tile
99.3
96.8
81.8
82.3
58.2
59.2
96.0
94.3
Toothbrush
93.9
66.3
78.3
Transistor
94.1
99.2
80.3
73.6
47.5
67.8
95.0
93.4
Wood
98.4
99.5
92.4
89.7
48.4
93.8
93.01
93.5
Zipper
56.3
51.2
Average
96.29
96,92
91.03
79.85
90.50
59.75
71.82
94.75
93.10
93.30
94,59
Table 9: Setting: New Few-shot Setting, K (number of shot)=4, Dataset: MVTec, Sampling Ratio:
0.01, Metries: Image AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bottle
99.7
99.8
94.2
95.8
93.9
92.1
99.6
99.4
Cable
94.1
95.2
91.2
61.3
61.3
68.4
97.4
76. 1
Capsule
66.2
74.5
56.2
48.7
47.4
51.7
66.3
72.4
Carpet
99.6
99.4
97.6
92.5
91.5
93.2
99.0
97.9
Grid
77.9
81.6
81.5
76.2
75.3
76.4
63.0
91.2
Hazelnut
99.9
99.5
99.4
95.6
91.4
93.8
92.8
95.8
Leather
100
100
100
98.2
96.9
96.8
100
100
Meta Nut
95.9
96.2
91.3
62.5
60.8
65.3
94.7
94.6
Pill
89.3
88.2
85.6
61.8
61.3
62.8
89.0
80.8
Screw
63.9
68.9
49.2
52.9
52.8
55.7
54.1
56.6
Tile
100
100
99.8
91.3
90.4
90.8
100
95.5
Toothbrush
94.4
95.2
87.2
81.7
80.4
76.7
95.2
90.9
Transistor
98.5
99.2
95.8
52.5
82.4
79.3
98.4
85.2
Wood
97.4
97.9
98.6
51.4
95.8
94.2
97.4
98.6
Zipper
96.9
98.2
94.3
52.2
47.6
56.7
95.5
88.5
Average
92.22
92.92
84.97
71.64
74.77
76.93
89.49
88.23
13

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Published as a conference paper at ICLR 2023
Table 10: Setting: New Few-shot Setting, K (number of shot)=4, Dataset: MVTec, Sampling Ratio:
0.01, Metries: Pixel AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bottle 98.6
99.8
93.6
86.9
84.9
81.8
98.6
98.4
Cable
96.6
96.9
89.1
78.7
52.2
66.2
97.9
92.7
Capsule
97.7
97.9
86.2
80.1
59.3
78.4
97.7
97.6
Carpet
99.1
99.6
98.2
95.0
60.6
74.8
99.0
98.9
Grid
81.9
82.3
82.5
76.1
61.8
76.9
70.6
85.7
Hazelnut
98.3
99.1
98.5
89.1
74.9
65.2
97.0
98.0
Leather
99.3
99.6
99.3
89.3
75.3
86.7
96.9
99.1
Meta Nut
96.8
98.1
89.9
60.2
51.8
69.2
97.0
97.8
Pill
97.0
97.5
91.6
58.2
50.6
70.4
96.9
97.4
Screw
93.8
96.5
96.8
71.3
51.9
60.9
92.1
95.0
Tile
95.7
96.7
82.3
82.4
58.5
59.5
96.0
94.9
Toothbrush
98.8
98.9
94.2
76.9
66.9
78.9
98.8
98.5
Transistor
94.1
99.3
80.5
74.2
57.5
67.9
95.0
93.8
Wood
93.2
99.5
92.6
90.4
48.9
94.2
93.1
94.7
Zipper
98.4
99.3
94.8
93.8
56.4
52.3
98.3
94.0
Average
95.95
97.40
91.34
80.17
60.77
72.22
94.99
95.77
Table Setting: New Few-shot Setting, K (number of shot)=8, Dataset: MVTee, Sampling Ratio:
0.01, Metries: Image AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bottle
100
99.8
95.1
95.9
94.1
92.8
99.6
99.8
Cable
94.1
95.2
91.8
63.5
62.6
69.2
97.4
80.6
Capsule
89.7
90.5
69.5
58.9
57.8
58.5
85.3
76.3
Carpet
98.5
99.5
97.6
92.7
91.6
93.8
99.0
98.5
Grid
92.7
92.3
85.6
77.3
76.9
77.9
83.1
91.5
Hazelnut
100
100
99.4
96.5
91.8
94.2
99.8
96.5
Leather
100
100
100
98.7
97.2
97.2
100
100
Meta Nut
96.8
97.9
92.3
68.9
61.3
65.6
95.1
98.3
Pill
90.1
91.1
88.9
63.9
64.2
63.6
89.6
80.6
Screw
79.4
80.1
65.4
56.4
55.9
59.3
74.1
63.4
Tile
99.3
100
99.8
91.8
91.2
91.2
100
97.4
Toothbrush
94.6
95.1
88.9
82.9
82.3
77.9
96.8
98.5
Transistor
98.2
99.2
96.2
58.9
84.6
81.2
98.9
93.4
Wood
98.7
98.9
98.9
61.3
95.8
95.6
97.5
99.4
Zipper
99.0
99.2
94.5
62.5
57.2
58.9
98.4
94.0
Average
95.41
95.92
90.93
75.34
77.63
78.46
94.31
91.21
14

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Published as a conference paper at ICLR 2023
Table 12: Setting: New Few-shot Setting, K (number of shot)=8, Dataset: MVTec, Sampling Ratio:
0.01, Metrics: Pixel AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bottle 98.6
99.8
93.6
87.1
85.2
82.1
98.7
97.3
Cable
97.0
97.2
89.2
78.9
53.3
68.2
98.3
94.9
Capsule
98.3
98.5
86.5
80.2
59.3
78.5
98.4
98.2
Carpet
99.1
99.7
98.4
95.1
60.7
79.2
99.2
98.9
Grid
82.5
83.7
82.8
77.2
61.8
76.9
71.5
88.7
Hazelnut
98.4
99.2
98.6
89.5
74.9
65.5
97.2
98.5
Leather
99.4
99.6
99.4
90.2
75.3
86.9
99.4
98.9
Meta Nut
97.3
98.9
89.9
60.5
54.6
69.5
97.5
96.9
Pill
98.1
98.4
91.7
58.2
55.7
70.5
98.1
97.8
Screw
94.2
96.6
96.9
71.4
52.3
61.9
92.5
97.1
Tile
96.8
97.4
83.4
82.5
58.9
60.8
96.3
95.2
Toothbrush
99.2
99.2
94.5
77.2
66.9
79.1
99.2
98.7
Transistor
95.2
99.4
81.5
74.5
58.2
67.9
95.7
96.8
Wood
93.8
99.7
92.7
90.4
49.2
94.5
93.4
94.6
Zipper
98.6
99.7
94.9
94.2
57.8
52.8
98.6
97.4
Average 96.43
97.80
91.60
80.47
61.61
72.95
95.60
96.67
Table 13: Setting: New Few-shot Setting, K (number of shot)=1, Dataset: MPDD, Sampling Ratio:
0.01, Metrics: Image AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bracket Black
64.8
65.9
64.1
62.1
93.2
91.2
58.2
Bracket Brown
75.0
76.8
65,4
59.2
59.8
58.3
70.6
Bracket White
88.6
89.2
68.2
68.2
43.2
44.7
69.3
Connector
98.3
98.7
58.5
58.5
90.5
92.5
59.0
Metal Plate
99.9
99.9
62.1
63.2
71.2
74.3
64.1
Tubes
76.6
77.8
34.2
33.8
65.1
44.2
34.1
Average
83.87
84.72
58.75
57.50
59.20
67.53
59.22
57.8
Table 14: Setting: New Few-shot Setting, K (number of shot)=1, Dataset: MPDD, Sampling Ratio:
0.01, Metries: Pixel AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bracket Black
91.7
92.3
75.2
72.4
74.5
72.5
78.8
Bracket Brown
91.8
92.2
77.2
71.8
72.9
72.3
76.8
Bracket White
97.0
97.3
69.8
65.4
63.1
61.3
67.8
Connector
97.0
97.5
88.9
82.4
82.1
81.7
85.0
Metal Plate
98.1
98.9
83.1
75.2
83.2
75.4
84.1
Tubes
92.4
92.8
71.7
76.2
74.5
76.1
78.2
Average
94.67
95.17
77.65
73.90
75.05
73.22
78.45
Table 15: Setting: New Few-shot Setting, K (number of shot)=2, Dataset: MPDD, Sampling Ratio:
0.01, Metries: Image AUROC
Category
AR).(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD)
Bracket Black
66.8
67.0
54.3
62.4
94.5
91.7
58,6
63.3
Bracket Brown
76.1
77.2
66.8
59.5
62.3
58.8
70.7
59.4
Bracket White
87.2
89.4
68.7
67.2
53.8
55.6
70.4
55.6
Connector
98.6
98.9
58.5
59.2
51.6
53.7
59.2
73.0
Metal Plate
99.9
99.9
62.7
64.2
62.4
65.2
64.1
61.7
Tubes
79.2
79.8
40.7
35.6
49.6
45.9
34.3
67.1
Average
84.63
85.37
58.62
58.02
62.37
61.82
59.55
63.35
15

---

Published as a conference paper at ICLR 2023
Table Setting: New Few-shot Setting, K (number of shot)=2, Dataset: MPDD, Sampling Ratio:
0.01, Metrics: Pixel AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAl>
Bracket Black
92.1
92.5
75.8
72.8
75.1
75.4
78.9
Bracket Brown
91.9
92.6
77.5
71.9
73.2
73.4
76.9
Bracket White
97.1
97.5
70.8
72.4
64.2
62.4
68.1
Connector
97.2
97.7
88.2
82.8
83.4
82.3
85.2
Metal Plate
98.4
99.1
84.3
75.9
83.2
76.5
86.3
Tubes
92.6
93.1
72.8
76.8
75.6
77.1
79.5
Average
94.88
95.42
78.23
75.43
75.78
74.52
79.15
93.2
Table 17: Setting: New Few-shot Setting, K (number of shot)=4, Dataset: MPDD, Sampling Ratio:
0.01, Metries: Image AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bracket Black
66.9
67.8
54.9
62.4
94.5
91.9
58.9
63.8
Bracket Brown
76.5
77.8
66.8
59.5
62.4
59.0
70.8
66.1
Bracket White
87.5
89.6
71.1
67.5
54.2
55.7
70.7
59.3
Connector
98.9
98.9
58.7
59.5
52.1
54.4
59.4
77.2
Metal Plate
99.9
99.9
62.9
64.9
62.4
65.5
64.4
78.6
Tubes
79.6
80.0
41.1
35.9
50.2
46.2
34.5
67.5
Average 84.88
85.67
59.25
58.28
62.62
62.12
59.78
68.75
Table 18: Setting: New Few-shot Setting, K (number of shot)=4, Dataset: MPDD, Sampling Ratio:
0.01, Metrics: Pixel AUROC
Category 92.7
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD>
Bracket Black
92.7
75.9
72.9
75.3
75.9
79.1
Bracket Brown
92.1
92.9
77.9
72.3
73.5
74.8
77.3
Bracket White
97.5
97.8
71.2
72.9
64.7
64.5
69.3
Connector
97.5
98.1
88.8
82.9
84.2
82.4
86.4
Metal Plate
98.5
99.2
84.8
76.9
83.5
77.2
86.7
Tubes
92.7
93.5
73.5
77.2
75.8
78.1
80.1
Average
95.17
95.70
78.68
75.85
76.17
75.48
79.82
93.9
Table 19: Setting: New Few-shot Sctting, K (number of shot)=8, Dataset: MPDD, Sampling Ratio:
0.01, Metries: Image AUROC
Category
Aug.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bracket Black
67.1
68.2
55.2
62.4
94.6
92.2
59.2
67.3
Bracket Brown
76.8
78.5
66.9
59.8
62.7
59.2
70.9
69.5
Bracket White
87.9
89.9
79.4
67.6
54.7
55.9
70.5
61.4
Connector
98.9
99.1
58.9
59.9
52.7
54.6
59.6
84.9
Metal Plate
99.9
99.9
63.1
65.2
63.2
65.8
64.7
80.2
Tubes
79.8
80.3
41.7
36.2
50.8
46.7
34.8
67.9
Average 85.07
85.98
60.87
58.52
63.12
62.40
59.95
71.87
Table 20: Setting: New Few-shot Setting, K (number of shot)=8, Dataset: MPDD, Sampling Ratio:
0.01, Metrics: Pixel AUROC
Category 92.9
ARg.(R)
GraphCore
CFA
SPADE
STPM
RD4AD
PatchCore
RegAD
Bracket Black
92.9
76.2
73.1
76.3
76.2
79.6
Bracket Brown
92.3
93.1
77.9
72.6
74.2
75.1
77.5
Bracket White
97.9
98.2
71.8
73.1
64.9
64.6
70.2
Connector
98.1
98.3
89.1
83.1
84.5
82.6
87,1
Metal Plate
98.7
99.3
85.2
77.2
83.7
77.5
86.9
Tubes
92.9
93.4
73.9
78.1
76.1
78.3
80.5
Average
95.47
95.87
79.02
76.20
76.62
75.72
80.30
95.10
16

---

Published as a conference paper at ICLR 2023
6.3 ARCHITECTURE DETAILS
Table 21: The architecture details of GraphCore
Stage
Output Size
GraphCore
Stem
Conv
D 48
Stage
2
K 9
Downsample
#
Conv
D 96
Stage 2
# w
X 2
K 9
Downsample
H w
Conv
T6 T6
D = 240
Stage
H W
x
16 16
K = 9
Downsample
32 32
x
W
Conv
384
Stage
W
32
32
K Pooling and MLP
Hcad
In Table 21 D represents the feature dimension, whereas K represents the number of neighbors in GraphCore. H W rrppresents the size of the input image. We adapt GCN into the the pyramid architecture Wang et al. (b021b). The training epochs is 300. The optimizer is Adam Loshchilov Hutter The batch size is 128. The initial learning rate 0.005. The learning rate schedule is Cosine. The warmup epochs is 50. The weight decay is 0.05. The loss function is the cross entropy loss function.
6.4 ABLATION STADIES
Table 22: Ablation study for memory bank size and inference speed with respect shot
Method
Memory Bank Size (Average)
Inference speed (Average)
PatchCore
1.6M
0.0316s
(Rg.(R)+ PatchCore
1.8M
0.0325s
GraphCore
1.2M
0.0299s
Table 23: Ablation study for memory bank size and inference speed with respect to 2 shot
Method
Memory Bank (Average)
Inference speed (Average)
PatchCore
3.2M
0.0327s
(Rg PatchCore 3.2M
0.0327s
GruphCore
1.8M
0.0287s
The statical result of Table and Table 2 clearly demonstrate the effectiveness of GraphCore, especially for memory bank size and its inference speed.
The statistical results presented Tables 24 and 25 demonstrate that the rotation method outper- forms the other augmentation techniques. We believe this indicates that the majority of industrial anomaly image datasets can be augmented by rotation. the future, we believe that there will more complex and realistic industrial anomaly image dataset that cannot be overcome by rotation.
17

---

Published as a conference paper at ICLR 2023
Table 24: Ablation study with respect to Dataset: MVTec 2D, sampling rate: 0.01, Metrics: image- level AUROC, number of shot is
Augmentation Type
Aug
PatchCore
Flipping
81.4
Translation
83.6
Scaling
82.3
Rotation
87.4
Table 25: Ablation study with respect to Dataset: MVTec 2D), sampling rate: 0.01, Metrics: image- level AUROC, number of shot is 2.
Augmentation Type
Aug
PatchCore
Flipping
83.7
Translation
85.6
Scaling
90.2
Rotation
90.5
18


Reviews:
[{"review_id": "gCD3LXAt3R", "reviewer": "Reviewer_Mmca", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper presents a fewshot anomaly detection (FSAD) method based on the PatchCore framework. The main novelty in this paper is the proposed visual isometric invariant feature (VIIF) based on GNN  architecture in the proposed GraphCore method for few-shot anomaly detection. They show the proposed method can achieve SOTA results on two public datasets, including MVTec dataset.", "summary_of_the_review": "In general, the proposed idea of using VIIF in the PatchCore framework is novel and useful. The experimental results show significant improvement over SOTA methods for FSAD experiments on public datasets. Unfortunately, the description of the GCN fopr VIIF is not clear. I am willing to raise my rating if more details can be provided for the GCN model in the paper.", "strength_and_weaknesses": "Strengths:\n1. The idea of extracting visual isometric invariant features based on GNN architecture and applying it to the PatchCore framework for FSAD is novel and suitable for practical applications in industry.\n\n2. The improvement of PatchCore with Aug.(R) memory bank is simple and useful to achieve better performance in accuracy and speed.\n\n3. Experimental results on two public datasets by using the proposed methods show significant improvement over the previous SOTA methods for FSAD protocole.\n\n\nWeaknesses:\n1. The visual isometric invariant features (VIIF) is very critical in the proposed GraphCore method. Unfortunatley, the description on the VIIF is confusing. The equations (4)-(6) do not provide clear description on the GCN and some notations used in the equations are not well defined. In addition, there is no discussion on the training of the GCN model for computing the VIIF in the paper. What is the loss function for the network training?\n\n2. The paper claims the proposed GraphCore method can provide fast model training, but there is no experimental justification on this claim. \n\n3. How is the computational cost of the proposed methods compared to the competing SOTA methods, such as those different versions of PatchCore method? What are the sizes of the memory banks for the proposed methods and different versions of PatchCore method used in their experimental comparison.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "CXMESuYlB5D", "reviewer": "Reviewer_Cvv9", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper focuses on unsupervised visual anomaly detection in a changeover-based few-shot learning setting. It is a fundamental problem since the changeover scenario requires an anomaly detection model to localize the anomalies in a few samples. Unlike previous meta-learning settings, the authors propose a native few-shot setting, i.e., the training dataset at most contains eight normal samples, to simulate the changeover-based situation. The authors first introduce the data augmentation methods into PatchCore , which can significantly surpass the sota models, like RegAD. Then they unlock the deep insight from Aug+PatchCore and propose a novel model, GraphCore. The motivation of GraphCore is to utilize the rotation-invariant feature representation for each patch and replace ResNet with a GCN model. The authors evaluate their methods on multiple datasets and obtain a high performance of GraphCore in the changeover-based few-shot setting. ", "summary_of_the_review": "This is a vital paper, and I recommend it for acceptance. It addresses changeover-based few-shot anomaly detection problems in a practical manner. The authors propose a novel GraphCore model to extract visual isometric features. The extensive experiments verify the effectiveness of visual isometric features. However, the paper lacks the visualization of the training samples under one-shot and two-shot settings.  In total,  I would love to see GNN be more adapted to visual anomaly detection method, which boosts merging CV community with GNN community.\n", "strength_and_weaknesses": "Strengths\n\n+ The authors introduce one simple but effective method, Aug+PatchCore, which is well-suited for real-world challenges and provides valuable insight into the problems. The written logic is evident and easy to follow. I like the writing style of this paper and read it with great interest. \n+ The authors propose an excellent problem setup to address fundamental challenges from a changeover-based setting. Previously, few-shot anomaly detection often adopts meta-learning settings, ignoring fast converagence in industrial manufacturing. Fig.1 is easy to follow. Moreover, the assumptions are reasonable.\n+ The motivation is apparent and reasonable, where they find that the rotation-invariant features primarily increase the anomalies detection abilities. The authors utilize the isometric transformation property of GCN and propose a new feature extraction model, GraphCore. In addition, I believe that the introduction of GCN into anomaly detection for the first time is quite innovative.\n+ The experiments are sufficient to support their conclusions.\n\n\nWeaknesses\n\n+ Though the numerical results are convincing, I think the authors had better visualize the normal samples in their training dataset, especially in the few-shot setting. I suppose that the quality of normal samples or features predominantly affects the result in one-shot or two-shot setting. So they need to show us which normal samples for training. \n+  I encourage more data augmentation methods to sufficiently support their insights, not just for rotation augmentation. I think there is a need to explain why GraphCore could enhance the performance of other classes, like grid, bottle and even hazelnut. I suggest more data augmentation methods, e.g., flipping, translation or even scaling, should be conducted to prove the advantages of GraphCore. I think my suggestions could make this paper more substantial.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "3MLWYujAJJ", "reviewer": "Reviewer_oKEN", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "In this paper, the author proposes a new setting for addressing industrial-based few-shot anomaly detection (FSAD). Specifically, the author leverages pre-trained graph neural networks to extract efficient features of normal products and anomalies, called visual isometric invariant features. Besides, the two proposed prototypes, i.e., convolution feature with augmentation and vision isometric invariant GNN (GraphCore), illustrate the significance of the proposed rotation-invariant feature in the AD task. Finally, ablation studies and experimental results are conducted comprehensively on popular datasets MVtecAD and MPDD.\n", "summary_of_the_review": "Faced with the FSAD task in industrial manufacturing, the author proposes a simple setup and a solution called GraphCore that can capture rotation-invariant visual features. The effectiveness of the proposed method is validated through a comprehensive comparison of current FSAD methods, and the provided insights are well supported. This work is reasonable and solid overall.\n", "strength_and_weaknesses": "Strength:\n+ Based on the actual industrial scene, the author designs a simple but efficient anomaly detection paradigm .\n+ The author unlocks insights that the rotation-invariant visual features are critical for few-shot anomaly detection and then they propose a practical solution using graph neural networks.\n+ I appreciate that the author clarifies the differences between convolution and isometric invariant features.\n+ The experimental results show that the proposed model GraphCore has achieved significant performance with a considerable margin improvement.\n+ Both ablation studies and visualization results verify the effectiveness of rotation-invariant features.\n+ The paper is organized well and easy to understand.\n\nWeaknesses:\n- In Section 3.1, Dataset, the author lists three datasets. However, MVTec LOCO AD results are not reported in Section 3.2.\n- The author states that visual features extracted by GraphCore is efficient. However, there are no ablation studies, such as the number of features used or feature visualization, to demonstrate this point.\n- There are some typos as follows:\n (1) In Abstract, “we utilize of graph” should be “we utilize graph”.\n (2) In Section 2, Movtivation part, Fig. 5 has no the screw, and maybe Fig, 6?\n (3) In Figure 6, “Rotat” should be “Rotate”.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "-VBgz0RO3ea", "reviewer": "Reviewer_dzsB", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper addresses the industrial visual few-shot anomaly detection (AD) in real-world challenges. In this situation, only a few normal samples are used for training. This paper first summarizes the previous methods and then point out why they are not suitable for the real-world scenario. The paper selects PatchCore as the baseline and analyzes the effect of feature enhancement on the memory bank. Then, the paper uses GNN from a different perspective to make image features unaffected by rotation, thereby reducing the dependence on feature enhancement methods. The paper designs a model named GraphCore for the changeover problem in industrial manufacturing, and obtains the new SOTA of few-shot AD.", "summary_of_the_review": "The paper discusses anomaly detection challenges in the real world. The intent is plain. The comparison of the proposed strategy to previous approaches proves its effectiveness. This research, I believe, demonstrates the potential of GNN in the field of AD. However, the primary content need revisions to certain specifics.", "strength_and_weaknesses": "Strength:\n+ The scenario of the proposed few-shot AD is reasonable. It is concluded that using a small amount of training data is enough for achieving a satisfying performance.\n+ The paper is the first one to introduce GNN into the field of industrial few-shot AD.\n+ On the basis that feature rotation enhancement can bring gains to PatchCore, the paper designs a model named GraphCore to extract the most critical features of images without being affected by rotation. In this way, a small number of images can provide sufficient features for anomaly detection, reducing the size of memory bank.\n+ The experiments are extensive. The paper compares the proposed method with a variety of representative methods on multiple datasets in terms with different shot numbers. It clearly proves the effectiveness of the paper's method.\n\nWeaknesses:\n+ There are relatively few GraphCore visualization results, and each category should have at least one visualization graph.\n+ The structure of GNN is inadequately described. The author should provide additional details.\n+ Grammatical errors. In the abstract, \"provide a novel visual isometric invariant feature (VIIF) as anomaly measurement feature\" should be \"provide a novel visual isometric invariant feature (VIIF) as an anomaly measurement feature\". ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster