Title: Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Venue: NeurIPS 2024
Accepted: no
Keywords: Unsupervised Anomaly Detection

Unsupervised Learning

Multi-Class UAD

Unified Model
Abstract: Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images, serving as an alternative to the conventional one-class-one-model setup. Despite various advancements addressing this challenging task, the detection performance under the multi-class setting still lags far behind state-of-the-art class-separated models. Our research aims to bridge this substantial performance gap. In this paper, we introduce a minimalistic reconstruction-based anomaly detection framework, namely Dinomaly, which leverages pure Transformer architectures without relying on complex designs, additional modules, or specialized tricks. Given this powerful framework consisted of only Attentions and MLPs, we found four simple components that are essential to multi-class anomaly detection: (1) _Foundation_ _Transformers_ that extracts universal and discriminative features, (2) _Noisy_ _Bottleneck_ where pre-existing Dropouts do all the noise injection tricks, (3) _Linear_ _Attention_ that naturally cannot focus, and (4) _Loose_ _Reconstruction_ that does not force layer-to-layer and point-by-point reconstruction. Extensive experiments are conducted across three popular anomaly detection benchmarks including MVTec-AD, VisA, and the recently released Real-IAD. Our proposed Dinomaly achieves impressive image AUROC of 99.6\%, 98.7\%, and 89.3\% on the three datasets respectively, which is not only superior to state-of-the-art multi-class UAD methods, but also surpasses the most advanced class-separated UAD records.

Abstract

Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images, serving as an alternative to the conventional one-class-one-model setup. Despite various advancements addressing this challenging task, the detection performance under the multi-class setting still lags far behind state-of-the-art class-separated models. Our research aims to bridge this substantial performance gap. In this paper, we introduce a minimalistic reconstruction-based anomaly detection framework, namely Dinomaly, which leverages pure Transformer architectures without relying on complex designs, additional modules, or specialized tricks. Given this powerful framework consisted of only Attentions and MLPs, we found four simple components that are essential to multi-class anomaly detection: (1) Foundation Transformers that extracts universal and discriminative features, (2) Noisy Bottleneck where pre-existing Dropouts do all the noise injection tricks, (3) Linear Attention that naturally cannot focus, and (4) Loose Reconstruction that does not force layer-to-layer and point-by-point reconstruction. Extensive experiments are conducted across three popular anomaly detection benchmarks including MVTec-AD, VisA, and the recently released Real-IAD. Our proposed Dinomaly achieves impressive image AUROC of 99.6%, 98.7%, and 89.3% on the three datasets respectively, which is not only superior to state-of-the-art multi-class UAD methods, but also surpasses the most advanced class-separated UAD records.

1 Introduction

Unsupervised anomaly detection (UAD) aims to detect abnormal patterns from normal images and further localize the anomalous regions. Because of the diversity of potential anomalies and their scarcity, this task is proposed to model the accessible training sets containing only normal samples as an unsupervised paradigm. UAD has a wide range of applications, e.g., industrial defect detection, medical disease screening, and video surveillance, addressing the difficulty of collecting and labeling all possible anomalies in these scenarios.

Efforts on UAD attempt to learn the distribution of available normal samples. Most advanced methods 28 utilize networks pre-trained on large-scale datasets, e.g. ImageNet [1], for extracting discriminative 29 and informative feature representations. Specifically, Feature reconstruction [2; 3; 4] and feature 30 distillation methods [5; 6] are proposed to reconstruct features of pre-trained encoders, based on 31 the hypothesis that the networks trained on normal images can only construct normal regions, but 32 fail for unseen anomalous regions. Feature statistics methods [7; 8; 9] memorize and model all 33 anomaly-free features extracted from pre-trained networks in training, and compare them with the 34 test features during inference. Pseudo-anomaly methods [10; 11] generate pseudo defects or noises 35

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on normal images or features to imitate anomalies, converting UAD to supervised classification [11] 36 or segmentation tasks [10; 12]. 37

Conventional works on UAD build a separate model for each object category, as shown in Figure 1(a). 38 However, this one-class-one-model setting entails substantial storage overhead for saving models 39 [3], especially when the application scenario necessitates a large number of object classes. For 40 UAD methods, a compact boundary of normal patterns is vital to distinguish anomalies. Once the 41 intra-normal patterns become exceedingly complicated due to various classes, the corresponding 42 distribution becomes challenging to measure, consequently harming the detection performance. 43 Recently, UniAD [3] and successive studies have been proposed to train a unified model for multi44 class anomaly detection (MUAD), as shown in Figure 1(b). Under this setting, the "identity mapping" 45 that directly copies the input as the output regardless of normal or anomaly harms the performance of 46 conventional methods [3]. This phenomenon is caused by the diversity of multi-class normal patterns 47 that drive the network to generalize on unseen patterns. 48

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Within two years, a number of methods have been proposed to address MUAD, such as neighbormasked attention [3], synthetic anomalies [23], feature jitter [3], vector quantization [24], diffusion model [25; 26], and state space model (Mamba) [19]. However, there is still a non-negligible performance gap between the state-of-the-art (SoTA) MUAD methods and class-separated UAD methods, restricting the practicability of implementing unified models, as shown in Figure 1(c). In addition, previous methods employ modules and architectures delicately designed, which may not be straightforward, and consequently suffer from limited universality and usability.

In this work, we aim to catch up with the performance of class-separated anomaly detection models 56 using a multi-class unified model, namely Dinomaly. To begin with, we build a reconstruction-based 57 UAD framework that consists of only vanilla Transformer blocks [27], i.e. Self-Attentions and 58 Multi-Layer Perceptrons (MLPs). Within this framework, we propose four simple but essential 59 elements that boost Dinomaly to perform equal to or better than SoTA conventional class-separated 60 models. First, we show that self-supervised pre-trained Vision Transformers (ViT) [28], especially the 61 DINO family [29; 30], serve as powerful feature encoders to extract discriminative representations as 62

reconstruction objects. Second, as an alternative to carefully designed pseudo anomaly and feature 63 noise, we propose to activate the out-of-the-box Dropout in an MLP to prevent the network from 64 restoring both normal and anomalous patterns, which is previously referred to as identity mapping. 65 Third, we propose to utilize the "side effect" of Linear Attention (a computation-efficient counterpart 66 of Softmax Attention) that makes it hard to focus on local regions, to further alleviate the issue of 67 identity mapping. Fourth, previous methods adopt layer-to-layer and region-by-region reconstruction 68 schemes, distilling a decoder that can well mimic the behavior of the encoder even for anomalous 69 regions. Therefore, we propose to loosen the reconstruction constraints by grouping multiple layers 70 as a whole and discarding well-reconstructed regions during optimization. 71

To validate the effectiveness of the proposed Dinomaly under MUAD setting, we conduct extensive 72 experiments on three widely used industrial defect detection benchmarks, i.e., MVTec AD [20] 73 (15 classes), VisA [21] (12 classes), and recently released Real-IAD (30 classes). Notably, we 74 achieve unprecedented image-level AUROC of 99.6%, 98.7%, and 89.3% on MVTec AD, VisA, and 75 Real-IAD, respectively, which surpasses previous SoTA methods by a large margin. 76

Related works are presented in Appendix A.1. 77

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2 Method

2.1 Dinomaly Framework

'What I cannot create, I do not understand' --Richer Feynman

The ability to recognize anomalies from what we know is an innate human capability, serving as a vital pathway for us to explore the world. Similarly, we construct a reconstruction-based framework that relies on the epistemic characteristic of artificial neural networks. Dinomaly consists of an encoder, a bottleneck, and a reconstruction decoder, as shown in Figure 2. Without loss of generality, a standard ViT-Base/14 network [28] with 12 Transformer layers is used as the encoder, extracting informative feature maps with different semantic scales. The bottleneck is a simple MLP (a.k.a. feed-forward network, FFN) that collects the feature representations of the encoder's 8 middle-level layers. The decoder is similar to the encoder, consisting of 8 Transformer layers. During training, the decoder learns to reconstruct the middle-level features of the encoder by maximizing the cosine similarity between feature maps. During inference, the decoder is expected to reconstruct normal regions of feature maps but fails for anomalous regions as it has never seen such samples.

Foundation Transformers. Foundation models, especially ViTs [28; 31] pre-trained on large-scale datasets, serve as a basis and starting point for specific computer vision tasks. Such networks employ self-supervised learning schemes such as contrastive learning (MoCov3 [32], DINO [29]), masked image modeling (MAE [33], SimMIM [34], BEiT [35]), and their combination (iBOT [36], DINOv2 [30]), producing universal features suitable for image-level visual tasks (image classification, instance retrieval) and pixel-level visual tasks (depth estimation, semantic segmentation).

Because of the lack of supervision in UAD, most advanced methods adopt pre-trained networks to extract discriminative features. Recent works [37; 17; 38] have discovered the advantage of robust and universal features of self-supervised models over domain-specific ImageNet features in anomaly detection tasks. In this work, we further utilize the up-to-date Transformer foundation, i.e., DINOv2 with registers [39], as the encoder of Dinomaly.

2.2 Noisy Bottleneck.

'Dropout is all you need. '

Generalization ability is a merit of neural networks, allowing them to perform equally well on unseen 105 test sets. However, generalization is not so wanted in the context of unsupervised anomaly detection 106 that leverages the epistemic nature of neural networks. With the increasing diversity of images and 107 their patterns due to multi-class UAD settings, the decoder can generalize its reconstruction ability to 108 unseen anomalous samples, resulting in the failure of anomaly detection using reconstruction error. 109 This phenomenon is called "identity mapping" in previous works of literature [3; 23; 18]. 110

Adirect solution for identity mapping is to shift "reconstruction" to "restoration". Specifically, instead 111 of directly reconstructing the normal images or features given normal inputs, previous works propose 112

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to add perturbations as pseudo anomalies on input images [10; 40; 12] or feature representations 113 [3; 25] during network forward propagation; meanwhile, still let the decoder restore anomaly-free 114 images or features, formulating a denoising-like framework. However, such methods employ heuristic 115 and hand-crafted anomaly generation strategies, that are not universal across domains, datasets, and 116 methods. 117

In this work, we propose to activate the pre-existing Dropout in an MLP layer. Dropout, a popular 118 network element introduced by Hinton et al. [41] in 2014 to prevent overfitting, flourished in nearly 119 all neural network architectures to the present day, including Transformers. In Dinomaly, Dropout is 120 used to discard neural activations in the MLP bottleneck randomly. Instead of alleviating overfitting, 121 the role of Dropout in Dinomaly can be explained as feature noise and pseudo feature anomaly. 122 Although the decoder takes noisy features during training, it is encouraged to restore clean features 123 from the encoder. Without introducing any novel modules, this paradigm forces the decoder to restore 124 normal features given a test image with anomalies, in turn, mitigating identical mapping. 125

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2.3 Unfocused Linear Attention.

'One man's poison is another man's meat'

Softmax Attention is the key mechanism of Transformers, allowing the model to attend to different parts of its input token sequence. Formally, given an input sequence X ∈ R N × d with length N , Attention first transforms it into three matrices: the query matrix Q ∈ R N × d , the key matrix K ∈ R N × d , and the value matrix V ∈ R N × d :

where W Q , W K , W V ∈ R d × d are learnable parameters. By computing the attention map by the query-key similarity, the output of Attention is given as: 1

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Because the attention map is obtained by computing the similarity between all query-key pairs followed by row-wise Softmax, the computation complexity is O ( N 2 d ) .

Linear Attention was proposed as a promising alternative to reduce the computation complexity of vanilla Softmax Attention concerning the number of tokens [42]. By substituting Softmax operation with a simple activation function ϕ ( · ) (usually ϕ ( x ) = elu ( x ) + 1 ), we can change the computation order from ( QK T ) V to Q ( K T V ) . Formally, Linear Attention is given as:

where the computation complexity is reduced to O ( Nd 2 ) . The trade-off between complexity and expressiveness is a dilemma. Previous studies [43; 44] attribute Linear Attention's performance degradation on supervised tasks to its incompetence in focusing. Due to the absence of non-linear attention reweighting by Softmax operation, Linear Attention cannot concentrate on important regions related to the query, such as foreground and neighbors.

Back to MUAD, previous methods [3; 24] suggest adopting Attentions instead of Convolutions because Convolutions can easily learn identical mappings. Nevertheless, both operations are in danger of forming identity mapping by over-concentrating on corresponding input locations for producing the outputs:

how Attentions propagate information, we train two variants of Dinomaly using vanilla Softmax 150

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Attention or Linear Attention as the spatial mixer in the decoder and visualize their attention maps.

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Linear Attention spreads its attention across the whole image. This implies that Linear Attention,

position, reducing the chance of passing identical information of unseen patterns to the next layer 155

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during reconstruction. Of course, employing Linear Attention also benefits from less computation, free of performance drop.

2.4 Loose Reconstruction

'The tighter you squeeze, the less you have. '

Loose Constraint. Pioneers of feature-reconstruction/distillation UAD methods [5; 2] are inspired by knowledge distillation [45]. Most reconstruction-based methods distill specific encoder layers (e.g. 3 last layers of 3 ResNet stages) by the corresponding decoder layers [2; 5; 17] (Figure 4(a)) or the last decoder layer [3; 4] (Figure 4(b)). Intuitively, with more encoder-decoder feature pairs (Figure 4(c)), UAD model can utilize more information in different layers to discriminate anomalies. However, according to the intuition of knowledge distillation, the student (decoder) can better mimic the behavior of the teacher (encoder) given more layer-to-layer supervision, which is harmful for UAD models that detect anomalies by encoder-decoder discrepancy. This phenomenon is also embodied as identity mapping. Thanks to the top-to-bottom consistency of columnar Transformer layers, we propose to loosen the layer-to-layer constraint by adding up all feature maps of interested layers as a whole group, as shown in Figure 4(d). This scheme can be seen as loosening the layer-to-layer correspondence, so that the decoder is allowed to act much more differently from the encoder when the input pattern is unseen. Because features of shallow layers contain low-level visual characters that are helpful for precise localization, we can further group the features into the low-semantic-level group and high-semantic-level group, as shown in Figure 4(e).

Loose Loss. Following the above analysis, we also loosen the point-by-point reconstruction loss function by discarding some points in the feature map. Here, we simply borrow the hard-mining global cosine loss [18] that detaches the gradients of well-restored feature points with low cosine distance during training. Let f E and f D denotes (grouped) feature maps of encoder and decoder:

where F ( · ) denotes flatten operation, f D ( h, w ) represents the feature point at ( h, w ) , sg ( · ) 0 . 1 180 denotes shrink the gradient to one-tenth of the original 2 , D cos ( f D ( h, w ) , f E ( h, w )) < 181 90%[ D cos ( f D , f E )] batch selects 90% feature points with smaller cosine distance within a batch. 182 Total loss is the mean L global -hm of all encoder-decoder feature pairs. 183

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3 Experiments

3.1 Experimental Settings

Datasets. MVTec-AD [20] contains 15 objects (5 texture classes and 10 object classes) with a total of 3,629 normal images as the training set and 1,725 images as the test set (467 normal, 1258 anomalous). VisA [21] contains 12 objects. Training and test sets are split following the official splitting, resulting in 8,659 normal images in the training set and 2,162 images in the test set (962 normal, 1,200 anomalous). Real-IAD [22] is a large UAD dataset recently released, containing 30 distinct objects. We follow the official splitting that includes all views, resulting in 36,465 normal images in the training set and 114,585 images in the test set (63,256 normal, 51,329 anomalous).

Metrics. Following prior works [19; 17], we adopt 7 evaluation metrics. Image-level anomaly detection performance is measured by the Area Under the Receiver Operator Curve (AUROC), Average Precision (AP), and F 1 score under optimal threshold ( F 1 -max). Pixel-level anomaly localization is measured by AUROC, AP, F 1 -max and the Area Under the Per-Region-Overlap (AUPRO). The results of a dataset is the average of all classes.

Implementation Details. ViT-Base/14 (patchsize=14) pre-trained by DINOv2-R [39] is used as the encoder by default. The drop rate of Noisy Bottleneck is 0.2 by default and increases to 0.4 on the diverse Real-IAD. Loose constraint with 2 groups is employed, and the anomaly map is given by the mean per-point cosine distance of the 2 groups. The input image is first resized to 448 2 and then center-cropped to 392 2 , so the feature map ( 28 2 ) is large enough for anomaly localization. StableAdamW optimizer [46] with AMSGrad [47] (more stable than AdamW [48] in training) is utilized with lr =2e-3, β =(0.9,0.999) and wd =1e-4. The network is trained for 10,000 iterations (steps) on MVTec-AD and VisA, and 50,000 iterations on Real-IAD. More details are available in Appendix A.2.

3.2 Comparison to Multi-Class UAD SoTAs

We compare the proposed Dinomaly with the most advanced methods. Among them, RD4AD [2] based on feature reconstruction, SimpleNet [13] based on feature-level pseudo-anomaly, and DeSTSeg [12] based on feature reconstruction & pseudo anomaly are designed for conventional classseparated UAD settings. UniAD based on feature reconstruction, ReContrast [18] based on contrastive reconstruction, ViTAD [17] based on feature reconstruction & Transformer, DiAD [49] based on Diffusion reconstruction, and MambaAD [19] based on feature reconstruction & Mamba are designed for MUAD settings. Notably, ViTAD and MambaAD are contemporary arxiv preprints released within months. The intuitive comparison is already presented in Figure 1.

Experimental results are presented in Table 1, where Dinomaly surpasses compared methods by a large margin on all datasets and all metrics. On the most widely used MVTec-AD, Dinomaly produces image-level performance of 99.6 / 99.8 / 99.0 and pixel-level performance of 98.4 / 69.3 / 69.2 / 94.8 , outperforming previous SoTAs by 1.0 / 0.2 / 1.2 and 0.7 / 9.1 / 7.7 / 1.6 . This result declares that the image-level performance on the MVTec-AD dataset is nearly saturated under the MUAD setting. On the popular VisA, Dinomaly achieves image-level performance of 98.7 / 98.9 / 96.2 and pixel-level performance of 98.7 / 53.2 / 55.7 / 94.5 , outperforming previous SoTAs by 3.2 / 2.5 / 4.2 and 0.2 / 5.3 / 5.1 / 2.6 . On the Real-IAD that contains 30 classes, each with 5 camera views, we produce image-level and pixel-level performance of 89.3 / 86.8 / 80.2 and 98.8 / 42.8 / 47.1 / 93.9 , outperforming previous SoTAs by 3.0 / 2.2 / 3.2 and 0.3 / 4.9 / 5.4 / 3.4 , indicating our scalability to extremely complex scenarios. Per-class performances and qualitative visualization are presented in Appendix A.5 and A.6. In addition, adopting a larger backbone further improves the above performances, as presented in Table A2.

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3.3 Comparison to Class-Separated UAD SoTAs

We also compare our Dinomaly with class-separated SoTAs, as shown in Table 2. On MVTec-AD and VisA, our Dinomaly under MUAD setting is comparable to conventional SoTAs that build individual models for each class [2; 13; 8; 15]. In addition, Dinomaly is subjected to nearly no performance drop compared to its class-separated counterpart on these datasets. On the complicated Real-IAD that involves more images, classes, and views, class-separated Dinomaly sets new SoTA records. Multi-class Dinomaly presents moderate performance drop but is still comparable to class-separated SoTAs.

3.4 Ablation Study

Overall Ablation. We conduct experiments to verify the effectiveness of the proposed elements, i.e., Noisy Bottleneck (NB), Linear Attention (LA), Loose Constraint (LC), and Loose Loss (LL). The already-powerful baseline is Dinomaly with noiseless MLP bottleneck, Softmax Attention, dense layer-to-layer supervision, and global cosine loss [18]. Results on MVTec-AD and VisA are shown in Table 3 and Table A1, respectively. NB and LL can directly contribute to the model performance. LA and LC boost the performance with the presence of NB. The use of LC is not solely beneficial because

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LC makes the reconstruction too easy without injected noise. Combining some of the proposed elements boosts the performance of the baseline, while employing them all produces the best results. Noisy Rates . We conduct ablations on the discarding rate of the Dropouts in MLP bottleneck, as shown in Table 4. Experimental results demonstrate that Dinomaly is robust to different levels of dropout rate. Reconstruction Constraint. We quantitatively examine different reconstruction schemes presented in Figure 4. As shown in Table 5, group-to-group LC outperforms layer-to-layer supervision. On image-level metrics, 1-group LC with all layers added performs similarly to its 2-group counterpart that separates low-level and high-level layers; however, 1-group LC mixes low-level and high-level features which is harmful for anomaly localization. More ablations on scalability, input size, pre-trained foundations, etc., are presented in Appendix A.3.

4 Conclusion

Dinomaly, a minimalistic UAD framework, is proposed to address the under-performed MUAD models in this paper. We present four key elements in Dinomaly, i.e., Foundation Transformer, Noisy MLP Bottleneck, Linear Attention, and Loose Reconstruction, that can boost the performance under the challenging MUAD setting without fancy modules and tricks. Extensive experiments on MVTec AD, VisA, and Real-IAD demonstrate our superiority over previous model-unified multi-class models and even recent class-separated models, indicating the feasibility of implementing a unified model in complicated scenarios free of severe performance degradation.

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A Appendix / supplemental material

A.1 Related Work

Epistemic methods are based on the assumption that the networks respond differently during inference between seen input and unseen input. Within this paradigm, pixel reconstruction methods assume that the networks trained on normal images can reconstruct anomaly-free regions well, but poorly for anomalous regions. Auto-encoder (AE) [50; 51], variational auto-encoder (V AE) [52; 53], or generative adversarial network (GAN) [54; 55] are used to restore normal pixels. However, pixel reconstruction models may also succeed in restoring unseen anomalous regions if they resemble normal regions in pixel values or the anomalies are barely noticeable [2]. Therefore, feature reconstruction is proposed to construct features of pre-trained encoders instead of raw pixels [2; 3; 4]. To prevent the whole network from converging to a trivial solution, the parameters of the encoders are frozen during training. In feature distillation [5; 6], the student network is trained from scratch to mimic the output features of the pre-trained teacher network with the same input of normal images, also based on the similar hypothesis that the student trained on normal samples only succeed in mimicking features of normal regions.

Pseudo-anomaly methods generate handcrafted defects on normal images to imitate anomalies, converting UAD to supervised classification [11] or segmentation tasks [10]. Specifically, CutPaste [11] simulates anomalous regions by randomly pasting cropped patches of normal images. DRAEM [10] constructs abnormal regions using Perlin noise as the mask and another image as the additive anomaly. DeTSeg [12] employs a similar anomaly generation strategy and combines it with feature reconstruction. SimpleNet [13] introduces anomaly by injecting Gaussian noise in the pre-trained feature space. These methods deeply rely on how well the pseudo anomalies match the real anomalies, which makes it hard to generalize to different datasets.

Feature statistics methods [7; 8; 56; 9] memorize all normal features (or their modeled distribution) extracted by networks pre-trained on large-scale datasets and match them with test samples during inference. Since these methods require memorizing, processing, and matching nearly all features from training samples, they are computationally expensive in both training and inference, especially when the training set is large.

Multi-Class UAD . UniAD [3] first introduced multi-class anomaly detection, aiming to detect anomalies for different classes using a unified model. In this setting, conventional UAD methods often face the challenge of "identical shortcuts", where both anomaly-free and anomaly samples can be effectively recovered during inference [3]. It is caused by the diversity of multi-class normal patterns that drive the network to generalize on unseen patterns. This contradicts the fundamental assumption of epistemic methods. Many current researches focus on addressing this challenge [3; 24; 18; 57; 25]. UniAD [3] employs a neighbor-masked attention module and a feature-jitter strategy to mitigate these shortcuts. HVQ-Trans [24] proposes a vector quantization (VQ) Transformer model that induces large feature discrepancies for anomalies. LafitE [25] utilizes a latent diffusion model and introduces a feature editing strategy to alleviate this issue. DiAD [26] also employs diffusion models to address multi-class UAD settings. OmniAL [23] focuses on anomaly localization in the unified setting, preventing identical reconstruction by using synthesized pseudo anomalies. ViTAD [58] abstracts a unified feature-reconstruction UAD framework and employ Transformer building blocks. MambaAD [19] explores the recently proposed State Space Model (SSM), Mamba, in the context of multi-class UAD.

Scope of Application. In this work, we focus on sensory AD that detects regional or structural anomalies (common in practical applications such as industrial inspection, medical disease screening, etc.), which is distinguished from semantic AD . In sensory AD, normal and anomalous samples are the same objects except for anomaly, e.g. good cable vs. spoiled cable. In semantic AD, the class of normal samples and anomalous samples are semantically different, e.g. animals vs. vehicles. Semantic AD methods usually utilize and compare the global representation of images, which generally do not suffer from the issues of multi-class setting discussed in this paper..

A.2 Full Implementation Details

ViT-Base/14 (patch size=14) pre-trained by DINOv2 with registers (DINOv2-R) [39] is utilized as the encoder. The discard rate of Dropout in Noisy Bottleneck is 0.2 by default, which is increased to

0.4 for the diverse Real-IAD. Loose constraint with 2 groups and L global -hm loss are used by default. 489 The input image is first resized to 448 2 and then center-cropped to 392 2 , so that the feature map 490 ( 28 2 ) is large enough for localization. StableAdamW optimizer [46] with AMSGrad [47] is utilized 491 with lr (learning rate)=2e-3, β =(0.9,0.999), wd (weight decay)=1e-4 and eps =1e-10. The network 492 is trained for 10,000 iterations for MVTec-AD and VisA and 50,000 iterations for Real-IAD under 493 MUADsetting. The network is trained for 5,000 iterations on each class under the class-separated 494 UAD setting. The lr warms up from 0 to 2e-3 in the first 100 iterations and cosine anneals to 2e-4 495 throughout the training. The discarding rate in Equation 5 linearly rises from 0% to 90% in the first 496 1,000 iterations as warm-up (500 iters for class-separated setting). The anomaly map is obtained by 497 upsampling the point-wise cosine distance between encoder and decoder feature maps (averaging 498 if more than one pair or group). The mean of the top 1% pixels in an anomaly map is used as the 499 image anomaly score. All experiments are conducted with random seed=1 with cuda deterministic 500 for invariable weight initialization and batch order. Codes are implemented with Python 3.8 and 501 PyTorch 1.12.0 cuda 11.3, and run on NVIDIA GeForce RTX3090 GPUs (24GB). 502

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A.3 Additional Ablation Studies and Experiments

Ablations on VisA. Similar to Table 3 that conduct ablation experiments on MVTec-AD, we additionally run them on VisA for further validations. As shown in Table A1, proposed components of Dinomaly contribute to the AD performances on VisA as on MVTec-AD.

Scalability. Previous works [3; 2; 17] reported that AD methods do not follow the model "scaling law", i.e., larger models do not necessarily produce better performance. For example, RD4AD [2] found WideResNet50 better than WideResNet101 as the encoder backbone. ViTAD [17] found ViTSmall better than ViT-Base. We conduct experiments to probe the influence of the scale of backbone Transformers in Dinomaly. ViT-Small, ViT-Base (default), and ViT-Large pre-trained by DINOv2-R are used as the encoder, respectively. ViT-Small has 12 layers, so we take the [3,4,5,...10] th layer as the interested 8 middle layers, which is the same as default ViT-Base. ViT-Large has 24 layers, so we take the [5,7,9,...19] th layer as the interested 8 middle layers. The layer hyperparameters of the decoder, such as embedding dimension and numbers of attention heads, follow the hyperparameters of the corresponding encoder. Other training strategies are identical to default. As shown in Table A2, the MUAD performance of Dinomaly follows the "scaling law". Dinomaly equipped with ViT-Small already produces state-of-the-art results. ViT-Large further boosts Dinomaly to an unprecedented higher record.

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Input Size. The patch size of ViTs (usually 14 × 14 or 16 × 16 ) is much larger than the stem layer's down-sampling rate of CNNs (usually 4 × 4 ), resulting in smaller feature map size. For dense prediction tasks like semantic segmentation, ViTs usually employ a large input image size [30]. This practice holds in anomaly localization as well. In Table A3, we present the results of Dinomaly with different input resolutions. Following PatchCore [8], by default, we adopt center-crop preprocessing to reduce the influence of background, which can also cause unreachable anomalies at the edge of images. Experimental results demonstrate our robustness to input size. While small image size is enough for image-level anomaly detection, larger inputs are beneficial to anomaly localization. All experiments evaluate localization performance in a unified size of 256 × 256 for fairness.

Pre-Trained Foundations. The representation quality of the frozen backbone Transformer is of great significance to unsupervised anomaly detection. We conduct extensive experiments to probe the impact of different pre-training methods, including supervised learning and self-supervised learning. DeiT [59] is trained on ImageNet[1] in a supervised manner by distilling CNNs. MAE [33], BEiTv2 [35], and D-iGPT [60] are based on masked image modeling (MIM). Given input images with masked patches, MAE [33] is optimized to restore raw pixels; BEiTv2 [35] is trained to predict the token index of VQ-GAN and CLIP; D-iGPT [60] is trained to predict the features of CLIP model. DINO [29] is based on positive-pair contrastive learning (CL), which is also referred to as self-distillation. It trains the network to produce similar feature representations given two views (augmentations) of the same image. iBot [36] and DINOv2 [30] combine MIM and CL strategies, marking the SoTA of self-supervised foundation models. DINOv2-R [39] is a variation of DINOv2 that employs 4 extra register tokens.

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It is noted that most models are pre-trained with the image resolution of 224 × 224 , except that DINOv2 [30] and DINOv2-R [39] have extra a high-resolution training phase with 518 × 518 . However, directly using the pre-trained weights on a different resolution for UAD without fine-tuning like other supervised tasks can cause generalization problems. Therefore, by default, we still keep the feature size of all compared models to 28 × 28 , i.e., the input size is 392 × 392 for ViT-Base/14 and 448 × 448 for ViT-Base/16. Additionally, we train Dinomaly with the low-resolution input size of 224 × 224 . The results are presented in Table A4. Generally speaking, CL+MIM combined models outperform MIM and CL models. In addition, MIM-based models do not benefit from higher resolutions but suffer from them, indicating the lack of generalization on a different input size. Methods involving CL can better adapt to a higher resolution as they optimize the global representation of class tokens in pre-training, which is insensitive to input size. As expected, DINOv2 and DINOv2-R pre-trained on larger inputs can better benefit from higher resolution in Dinomaly. Because some methods, i.e., D-iGPT, DINO, and iBOT, produce similar results to DINOv2 in 224 × 224 , we expect that they also have the potential to be as powerful in Dinomaly if they are pre-trained in high-resolution.

Attention vs. Convolution. Previous works and this paper have proposed to leverage attentions instead of convolutions in UAD. Here, we conduct experiments substituting the attention in the decoder of Dinomaly by convolutions as the spatial mixers. Following MetaFormer [61], we employ Inverted Bottleneck block that consists of 1 × 1 conv, GELU activation, N × N deep-wise conv, and 1 × 1 conv, sequentially. The results are shown in Table A5, where Attentions outperform Convolutions, especially for pixel-level anomaly localization. In addition, utilizing convolutions in the decoder can still yield SoTA results, demonstrating the universality of the proposed Dinomaly.

Neighbour-Masking. Prior method [3] proposed to mask the keys and values in an n × n square centered at each query, in order to alleviate identity mapping in Attention. This mechanism can also be applied to Linear Attention as well. As shown in Table A5, neighbor-masking can further improve Dinomaly with both Softmax Attention and Linear Attention moderately.

Feature Noise. Prior method [3] proposed to perturb the encoder features by Feature Jitter, i.e. adding Gaussian noise with scale to control the noise magnitude. We evaluate the feature jitter strategy in the proposed Dinomaly by placing it at the beginning of Noisy Bottleneck. As shown in Table A6, both Dropout and Feature Jitter can be a good noise injector in Noisy Bottleneck. Meanwhile, Dropout is more robust to the noisy scale hyperparameter, and more elegant without introducing new modules.

Random Seeds. Due to limited computation resources, experiments in this paper are conducted for one run with random seed=1. Here, we conduct 5 runs with 5 random seeds on MVTec-AD. As shown in Table A7, Dinomaly is robust to randomness.

A.4 Limitation

Vision Transformers are known for their high computation cost, which can be a barrier to lowcomputation scenarios that require inference speed. Future research can be conducted on the efficiency of Transformer-based methods, such as distillation, pruning, and hardware-friendly attention mechanism (such as FlashAttention).

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As discussed in section A.1, Dinomaly is used for sensory AD that aims to detect regional anomalies in normal backgrounds. It is not suitable for semantic AD. Previous works have shown that methods designed for sensory AD usually fail to be competitive under semantic AD tasks [3; 2]. Conversely, methods designed for semantic AD do not perform well on sensory AD tasks [62; 37]. Future work can be conducted to unify these two tasks, but according to the "no free lunch" theorem, we believe that methods designed for specific anomaly assumption are likely to be more convincing.

Other special UAD settings, such as zero-shot UAD (vision-language model based) [63], few-shot UAD [64], UAD under noisy training set [65], are not included in this work.

A.5 Results Per-Category

For future research, we report the per-class results of MVTec-AD [20], VisA [21], and Real-IAD [22]. The performance of compared methods is drawn from MambaAD [19]. Thanks for their exhaustive reproducing. The results of image-level anomaly detection and pixel-level anomaly localization on MVTec-AD are presented in Table A8 and Table A9, respectively. The results of image-level anomaly detection and pixel-level anomaly localization on VisA are presented in Table A10 and Table A11, respectively. The results of image-level anomaly detection and pixel-level anomaly localization on Real-IAD are presented in Table A12 and Table A13, respectively.

A.6 Qualitative Visualization

We visualize the output anomaly maps of Dinomaly on MVTec-AD, VisA, and Real-IAD, as shown 597 in Figure A1, Figure A2, and Figure A3. It is noted that all visualized samples are randomly chosen 598 without artificial selection. 599

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98.5/99.6/97.2

99.8

/99.9/

99.4

95.7/98.7/93.2

95.9/98.8/94.9

99.4/99.9/98.3

99.8

/99.9/

99.4

99.8

/

100.

/98.9

Grid

98.0/99.4/96.5

98.2/99.5/97.3

97.6/99.2/96.4

97.9/99.2/96.6

98.5/99.8/97.7

100.

/

100.

/

100.

99.9/

100.

/99.1

Leather

100.

/

100.

/

100.

100.

/

100.

/

100.

100.

/

100.

/

100.

99.2/99.8/98.9

99.8/99.7/97.6

100.

/

100.

/

100.

100.

/

100.

/

100.

Tile

98.3/99.3/96.4

99.3/99.8/98.2

99.3/99.8/98.8

97.0/98.9/95.3

96.8/99.9/98.4

98.2/99.3/95.4

100.

/

100.

/

100.

Wood

99.2/99.8/98.3

98.6/99.6/96.6

98.4/99.5/96.7

99.9/

100.

/99.2

99.7/

100.

/

100.

98.8/99.6/96.6

99.8/99.9/99.2

Mean

94.6/96.5/95.2

96.5/98.8/96.2

95.3/98.4/95.8

89.2/95.5/91.6

97.2/99.0/96.5

98.6/99.6/97.8

99.6

/

99.8

/

99.0

Method

→

RD4AD [2]

UniAD [3]

SimpleNet [13]

DeSTSeg [12]

DiAD [49]

MambaAD [19]

Dinomaly

Category

↓

CVPR'22

CVPR'23

CVPR'23

AAAI'24

Arxiv'24

Ours

Objects

Bottle

97.8/68.2/67.6/94.0

98.1/66.0/69.2/93.1

97.2/53.8/62.4/89.0

93.3/61.7/56.0/67.5

98.4/52.2/54.8/86.6

98.8/79.7/76.7/95.2

99.2

/

88.6

/

84.2

/

96.6

Cable

85.1/26.3/33.6/75.1

97.3/39.9/45.2/86.1

96.7/42.4/51.2/85.4

89.3/37.5/40.5/49.4

96.8/50.1/57.8/80.5

95.8/42.2/48.1/90.3

98.6

/

72.0

/

74.3

/

94.2

Capsule

98.8

/43.4/50.0/94.8

98.5/42.7/46.5/92.1

98.5/35.4/44.3/84.5

95.8/47.9/48.9/62.1

97.1/42.0/45.3/87.2

98.4/43.9/47.7/92.6

98.7/

61.4

/

60.3

/

97.2

97.9/36.2/51.6/92.7

98.1/55.2/56.8/94.1

98.4/44.6/51.4/87.4

98.2/65.8/61.6/84.5

98.3/79.2/

80.4

/91.5

99.0/63.6/64.4/95.7

99.4

/

82.2

/76.4/

97.0

Metal Nut

94.8/55.5/66.4/91.9

62.7/14.6/29.2/81.8

98.0

/

83.1

/79.4/85.2

84.2/42.0/22.8/53.0

97.3/30.0/38.3/90.6

96.7/74.5/79.1/93.7

96.9/78.6/

86.7

/

94.9

Pill

97.5/63.4/65.2/95.8

95.0/44.0/53.9/95.3

96.5/72.4/67.7/81.9

96.2/61.7/41.8/27.9

95.7/46.0/51.4/89.0

97.4/64.0/66.5/95.7

97.8

/

76.4

/

71.6

/

97.3

Screw

99.4/40.2/44.6/96.8

98.3/28.7/37.6/95.2

96.5/15.9/23.2/84.0

93.8/19.9/25.3/47.3

97.9/

60.6

/

59.6

/95.0

99.5/49.8/50.9/97.1

99.6

/60.2/

59.6

/

98.3

Toothbrush

99.0

/53.6/58.8/92.0

98.4/34.9/45.7/87.9

98.4/46.9/52.5/87.4

96.2/52.9/58.8/30.9

99.0

/78.7/72.8/95.0

99.0

98.9/

51.5

/

62.6

/

95.3

Transistor

85.9/42.3/45.2/74.7

97.9

/59.5/

64.6

/

93.5

95.8/58.2/56.0/83.2

73.6/38.4/39.2/43.9

95.1/15.6/31.7/90.0

96.5/69.4/67.1/87.0

93.2/

59.9

/58.5/77.0

Zipper

98.5/53.9/60.3/94.1

96.8/40.1/49.9/92.6

97.9/53.4/54.6/90.7

97.3/64.7/59.2/66.9

96.2/60.7/60.0/91.6

98.4/60.4/61.7/94.3

99.2

/

79.5

/

75.4

/

97.2

Textures

Carpet

99.0/58.5/60.4/95.1

98.5/49.9/51.1/94.4

97.4/38.7/43.2/90.6

93.6/59.9/58.9/89.3

98.6/42.2/46.4/90.6

99.2/60.0/63.3/96.7

99.3

/

68.7

/

71.1

/

97.6

Grid

96.5/23.0/28.4/97.0

63.1/10.7/11.9/92.9

96.8/20.5/27.6/88.6/

97.0/42.1/46.9/86.8

96.6/66.0/64.1/94.0

99.2/47.4/47.7/97.0

99.4

/

55.3

/

57.7

/

97.2

Leather

99.3/38.0/45.1/97.4

98.8/32.9/34.4/96.8

98.7/28.5/32.9/92.7

99.5

/

71.5

/

66.5

/91.1

98.8/56.1/62.3/91.3

99.4/50.3/53.3/98.7

99.4/52.2/55.0/

97.6

Tile

95.3/48.5/60.5/85.8

91.8/42.1/50.6/78.4

95.7/60.5/59.9/90.6

93.0/71.0/66.2/87.1

92.4/65.7/64.1/

90.7

93.8/45.1/54.8/80.0

98.1

/

80.1

/

75.7

/90.5

Wood

95.3/47.8/51.0/90.0

93.2/37.2/41.5/86.7

91.4/34.8/39.7/76.3

95.9/

77.3

/

71.3

/83.4

93.3/43.3/43.5/

97.5

94.4/46.2/48.2/91.2

97.6

/72.8/68.4/94.0

Mean

96.1/48.6/53.8/91.1

96.8/43.4/49.5/90.7

96.9/45.9/49.7/86.5

93.1/54.3/50.9/64.8

96.8/52.6/55.5/90.7

97.7/56.3/59.2/93.1

98.4

/

69.3

/

69.2

/

94.8

Method

→

RD4AD [2]

UniAD [3]

SimpleNet [13]

DeSTSeg [12]

DiAD [49]

MambaAD

Dinomaly

Category

↓

CVPR'22

CVPR'23

CVPR'23

AAAI'24

Arxiv'24

Ours

pcb1

96.2/95.5/91.9

92.8/92.7/87.8

91.6/91.9/86.0

87.6/83.1/83.7

88.1/88.7/80.7

95.4/93.0/91.6

99.1

/

99.1

/

96.6

pcb2

97.8/97.8/94.2

87.8/87.7/83.1

92.4/93.3/84.5

86.5/85.8/82.6

94.2/93.7/89.3

99.3

/

99.2

/

97.0

pcb3

96.4/96.2/91.0

78.6/78.6/76.1

89.1/91.1/82.6

93.7/95.1/87.0

86.2/87.6/77.6

93.7/94.1/86.7

98.9

/

98.9

/

96.1

pcb4

99.9/99.9/99.0

98.8/98.8/94.3

97.0/97.0/93.5

97.8/97.8/92.7

99.6/99.5/97.0

99.9

/

99.9

/

98.5

99.8/99.8/98.0

75.9/ 1.5/76.8

79.9/79.8/72.7

85.9/82.5/73.1

76.6/69.0/71.0

85.7/85.2/78.8

91.6/89.8/81.6

98.0

/

97.6

/

94.2

macaroni2

88.3/84.5/83.8

71.6/71.6/69.9

68.3/54.3/59.7

68.9/62.1/67.7

62.5/57.4/69.6

81.6/78.0/73.8

95.9

/

95.7

/

90.7

capsules

82.2/90.4/81.3

55.6/55.6/76.9

74.1/82.8/74.6

87.1/93.0/84.2

58.2/69.0/78.5

91.8/95.0/88.8

98.6

/

99.0

/

97.1

candle

92.3/92.9/86.0

94.1/94.0/86.1

84.1/73.3/76.6

94.9/94.8/89.2

92.8/92.0/87.6

96.8/96.9/90.1

98.7

/

98.8

/

95.1

cashew

92.0/95.8/90.7

92.8/92.8/91.4

88.0/91.3/84.7

92.0/96.1/88.1

91.5/95.7/89.7

94.5/97.3/91.1

98.7

/

99.4

/

97.0

chewinggum

94.9/97.5/92.1

96.3/96.2/95.2

96.4/98.2/93.8

95.8/98.3/94.7

99.1/99.5/95.9

97.7/98.9/94.2

99.8

/

99.9

/

99.0

fryum

95.3/97.9/91.5

83.0/83.0/85.0

88.4/93.0/83.3

92.1/96.1/89.5

89.8/95.0/87.2

95.2/97.7/90.5

98.8

/

99.4

/

96.5

pipe_fryum

97.9/98.9/96.5

94.7/94.7/93.9

90.8/95.5/88.6

94.1/97.1/91.9

96.2/98.1/93.7

98.7/99.3/97.0

99.2

/

99.7

/

97.0

Mean

92.4/92.4/89.6

85.5/85.5/84.4

87.2/87.0/81.8

88.9/89.0/85.2

86.8/88.3/85.1

94.3/94.5/89.4

98.7

/

98.9

/

96.2

Method

→

RD4AD [2]

UniAD [3]

SimpleNet [13]

DeSTSeg [12]

DiAD [49]

MambaAD

Dinomaly

Category

↓

CVPR'22

CVPR'23

CVPR'23

AAAI'24

Arxiv'24

Ours

pcb1

99.4/66.2/62.4/

95.8

93.3/ 3.9/ 8.3/64.1

99.2/86.1/78.8/83.6

95.8/46.4/49.0/83.2

98.7/49.6/52.8/80.2

99.8

/77.1/72.4/92.8

99.5/

87.9

/

80.5

/95.1

pcb2

98.0/22.3/30.0/90.8

93.9/ 4.2/ 9.2/66.9

96.6/ 8.9/18.6/85.7

97.3/14.6/28.2/79.9

95.2/ 7.5/16.7/67.0

98.9

/13.3/23.4/89.6

98.0/

47.0

/

49.8

/

91.3

pcb3

97.9/26.2/35.2/93.9

97.3/13.8/21.9/70.6

97.2/31.0/36.1/85.1

97.7/28.1/33.4/62.4

96.7/ 8.0/18.8/68.9

99.1

/18.3/27.4/89.1

98.4/

41.7

/

45.3

/

94.6

pcb4

97.8/31.4/37.0/88.7

94.9/14.7/22.9/72.3

93.9/23.9/32.9/61.1

95.8/

53.0

/

53.2

/76.9

97.0/17.6/27.2/85.0

98.6/47.0/46.9/87.6

98.7

/50.5/53.1/

94.4

99.4/ 2.9/6.9/95.3

97.4/ 3.7/ 9.7/84.0

98.9/ 3.5/8.4/92.0

99.1/ 5.8/13.4/62.4

94.1/10.2/16.7/68.5

99.5/17.5/27.6/95.2

99.6

/

33.5

/

40.6

/

96.4

macaroni2

99.7/13.2/21.8/97.4

95.2/ 0.9/ 4.3/76.6

93.2/ 0.6/ 3.9/77.8

98.5/ 6.3/14.4/70.0

93.6/ 0.9/ 2.8/73.1

99.5/ 9.2/16.1/96.2

99.7

/

24.7

/

36.1

/

98.7

capsules

99.4/60.4/60.8/93.1

88.7/ 3.0/ 7.4/43.7

97.1/52.9/53.3/73.7

96.9/33.2/ 9.1/76.7

97.3/10.0/21.0/77.9

99.1/61.3/59.8/91.8

99.6

/

65.0

/

66.6

/

97.4

candle

99.1/25.3/35.8/94.9

98.5/17.6/27.9/91.6

97.6/ 8.4/16.5/87.6

98.7/39.9/45.8/69.0

97.3/12.8/22.8/89.4

99.0/23.2/32.4/

95.5

99.4

/

43.0

/

47.9

/95.4

cashew

91.7/44.2/49.7/86.2

98.6/51.7/58.3/87.9

98.9

/

68.9

/

66.0

/84.1

87.9/47.6/52.1/66.3

90.9/53.1/60.9/61.8

94.3/46.8/51.4/87.8

97.1/64.5/62.4/

94.0

chewinggum

98.7/59.9/61.7/76.9

98.8/54.9/56.1/81.3

97.9/26.8/29.8/78.3

98.8/

86.9

/

81.0

/68.3

94.7/11.9/25.8/59.5

98.1/57.5/59.9/79.7

99.1

/65.0/67.7/

88.1

fryum

97.0/47.6/51.5/93.4

95.9/34.0/40.6/76.2

93.0/39.1/45.4/85.1

88.1/35.2/38.5/47.7

97.6

/

58.6

/

60.1

/81.3

96.9/47.8/51.9/91.6

96.6/51.6/53.4/

93.5

pipe_fryum

99.1/56.8/58.8/

95.4

98.9/50.2/57.7/91.5

98.5/65.6/63.4/83.0

98.9/78.8/72.7/45.9

99.4

/

72.7

/

69.9

/89.9

99.1/53.5/58.5/95.1

99.2/64.3/65.1/95.2

Mean

98.1/38.0/42.6/91.8

95.9/21.0/27.0/75.6

96.8/34.7/37.8/81.4

96.1/39.6/43.4/67.4

96.0/26.1/33.0/75.2

98.5/39.4/44.0/91.0

98.7

/

53.2

/

55.7

/

94.5

Method

→

RD4AD [2]

UniAD [3]

SimpleNet [13]

DeSTSeg [12]

DiAD [49]

MambaAD

Dinomaly

Category

↓

CVPR'22

CVPR'23

CVPR'23

AAAI'24

Arxiv'24

Ours

audiojack

76.2/63.2/60.8

81.4/76.6/64.9

58.4/44.2/50.9

81.1/72.6/64.5

76.5/54.3/65.7

84.2/76.5/67.4

86.8

/

82.4

/

72.2

bottle cap

89.5/86.3/81.0

92.5/91.7/81.7

54.1/47.6/60.3

78.1/74.6/68.1

91.6/

94.0

/

87.9

92.8

/92.0/82.1

89.9/86.7/81.2

button battery

73.3/78.9/76.1

75.9/81.6/76.3

52.5/60.5/72.4

86.7/89.2/83.5

80.5/71.3/70.6

79.8/85.3/77.8

86.6/88.9/82.1

end cap

79.8/84.0/77.8

80.9/86.1/78.0

51.6/60.8/72.9

77.9/81.1/77.1

85.1/83.4/

84.8

78.0/82.8/77.2

87.0

/

87.5

/83.4

eraser

90.0/88.7/79.7

90.3

/

89.2

/

80.2

46.4/39.1/55.8

84.6/82.9/71.8

80.0/80.0/77.3

87.5/86.2/76.1

90.3

/87.6/78.6

fire hood

78.3/70.1/64.5

80.6/74.8/66.4

58.1/41.9/54.4

83.3/

81.7

/

80.5

79.3/72.5/64.8

83.8

/76.2/69.5

mint

65.8/63.1/64.8

67.0/66.6/64.6

52.4/50.3/63.7

58.4/55.8/63.7

76.7

/

76.7

/

76.0

70.1/70.8/65.5

73.1/72.0/67.7

mounts

88.6/79.9/74.8

87.6/77.3/77.2

58.7/48.1/52.4

74.7/56.5/63.1

75.3/74.5/

82.5

86.8/78.0/73.5

90.4

/

84.2

/78.0

pcb

79.5/85.8/79.7

81.0/88.2/79.1

54.5/66.0/75.5

82.0/88.7/79.6

86.0/85.1/85.4

89.1/93.7/84.0

92.0

/

95.3

/

87.0

phone battery

83.6/80.0/71.6

51.6/43.8/58.0

83.3/81.8/72.1

82.3/77.7/75.9

90.2/88.9/80.5

92.9

/

91.6

/

82.5

plastic nut

80.3/68.0/64.4

80.0/69.2/63.7

59.2/40.3/51.8

83.1/75.4/66.5

71.9/58.2/65.6

87.1/80.7/70.7

88.3

/

81.8

/

74.7

plastic plug

81.4/75.9/67.6

48.2/38.4/54.6

71.7/63.1/60.0

88.7/

89.2

/

90.9

85.7/82.2/72.6

90.5

/86.4/78.6

porcelain doll

86.3/76.3/71.5

85.1/75.2/69.3

66.3/54.5/52.1

78.7/66.2/64.3

72.6/66.8/65.2

88.0

/

82.2

/

74.1

85.1/73.3/69.6

regulator

66.9/48.8/47.7

56.9/41.5/44.5

50.5/29.0/43.9

79.2/63.5/56.9

72.1/71.4/

78.2

69.7/58.7/50.4

85.2

/

78.9

/69.8

rolled strip base

97.5/98.7/94.7

98.7/99.3/96.5

59.0/75.7/79.8

96.5/98.2/93.0

68.4/55.9/56.8

98.0/99.0/95.0

99.2

/

99.6

/

97.1

sim card set

91.6/91.8/84.8

89.7/90.3/83.2

63.1/69.7/70.8

95.5/96.2/

89.2

72.6/53.7/61.5

94.4/95.1/87.2

95.8

/

96.3

/88.8

switch

84.3/87.2/77.9

85.5/88.6/78.4

62.2/66.8/68.6

90.1/92.8/83.1

73.4/49.4/61.2

91.7/94.0/85.4

97.8

/

98.1

/

93.3

tape

96.0/95.1/87.6

97.2

/

96.2

/

89.4

49.9/41.1/54.5

94.5/93.4/85.9

73.9/57.8/66.1

96.8/95.9/89.3

96.9/95.0/88.8

terminalblock

89.4/89.7/83.1

87.5/89.1/81.0

59.8/64.7/68.8

83.1/86.2/76.6

62.1/36.4/47.8

96.1/96.8/90.0

96.7

/

97.4

/

91.1

toothbrush

82.0/83.8/77.2

78.4/80.1/75.6

65.9/70.0/70.1

83.7/85.3/79.0

91.2

/

93.7

/

90.9

85.1/86.2/80.3

90.4/91.9/83.4

toy

69.4/74.2/75.9

68.4/75.1/74.8

57.8/64.4/73.4

70.3/74.8/75.4

66.2/57.3/59.8

83.0/87.5/79.6

85.6/89.1/81.9

toy brick

63.6/56.1/59.0

77.0

/

71.1

/66.2

58.3/49.7/58.2

73.2/68.7/

63.3

68.4/45.3/55.9

70.5/63.7/61.6

72.3/65.1/63.4

transistor1

91.0/94.0/85.1

93.7/95.9/88.9

62.2/69.2/72.1

90.2/92.1/84.6

73.1/63.1/62.7

94.4/96.0/89.0

97.4

/

98.2

/

93.1

u block

89.5/85.0/74.2

88.8/84.2/75.5

62.4/48.4/51.8

75.2/68.4/67.9

89.7/

85.7

/

75.3

89.9

/84.0/75.2

usb

84.9/84.3/75.1

78.7/79.4/69.1

57.0/55.3/62.9

87.8/88.0/78.3

58.9/37.4/45.7

92.0

/

92.2

/

84.5

92.0

/91.6/83.3

usb adaptor

71.1/61.4/62.2

76.8/71.3/64.9

47.5/38.4/56.5

80.1/

74.9

/67.4

76.9/60.2/67.2

79.4/76.0/66.3

81.5

/74.5/

69.4

vcpill

85.1/80.3/72.4

87.1/84.0/74.7

59.0/48.7/56.4

83.8/81.5/69.9

64.1/40.4/56.2

88.3/87.7/77.4

92.0

/

91.2

/

82.0

wooden beads

81.2/78.9/70.9

78.4/77.2/67.8

55.1/52.0/60.2

82.4/78.5/73.0

62.1/56.4/65.9

82.5/81.7/71.8

87.3

/

85.8

/

77.4

woodstick

76.9/61.2/58.1

80.8/72.6/63.6

58.2/35.6/45.2

80.4/69.2/60.3

74.1/66.0/62.1

80.4/69.0/63.4

84.0

/

73.3

/

65.6

zipper

95.3/97.2/91.2

98.2/98.9/95.3

77.2/86.7/77.6

96.9/98.1/93.5

86.0/87.0/84.0

99.2

/

99.6

/

96.9

99.1/99.5/96.5

Mean

82.4/79.0/73.9

83.0/80.9/74.3

57.2/53.4/61.5

82.3/79.2/73.2

75.6/66.4/69.9

86.3/84.6/77.0

89.3

/

86.8

/

80.2

Method

→

RD4AD [2]

UniAD [3]

SimpleNet [13]

DeSTSeg [12]

DiAD [49]

MambaAD [19]

Dinomaly

Category

↓

CVPR'22

CVPR'23

CVPR'23

AAAI'24

Arxiv'24

Ours

audiojack

96.6/12.8/22.1/79.6

97.6/20.0/31.0/83.7

74.4/ 0.9/ 4.8/38.0

95.5/25.4/31.9/52.6

91.6/ 1.0/ 3.9/63.3

97.7/21.6/29.5/83.9

98.7

/

48.1

/

54.5

/

91.7

bottle cap

99.5/18.9/29.9/95.7

99.5/19.4/29.6/96.0

85.3/ 2.3/ 5.7/45.1

94.5/25.3/31.1/25.3

94.6/ 4.9/11.4/73.0

99.7

/30.6/34.6/97.2

99.7

/

32.4

/

36.7

/

98.1

button battery

97.6/33.8/37.8/86.5

96.7/28.5/34.4/77.5

75.9/ 3.2/ 6.6/40.5

98.3/

63.9

/

60.4

/36.9

84.1/ 1.4/ 5.3/66.9

98.1/46.7/49.5/86.2

99.1

/46.9/56.7/

92.9

end cap

96.7/12.5/22.5/89.2

95.8/ 8.8/17.4/85.4

63.1/ 0.5/ 2.8/25.7

89.6/14.4/22.7/29.5

81.3/ 2.0/ 6.9/38.2

97.0/12.0/19.6/89.4

99.1

/

26.2

/

32.9

/

96.0

eraser

99.5

/30.8/36.7/96.0

99.3/24.4/30.9/94.1

80.6/ 2.7/ 7.1/42.8

95.8/52.7/53.9/46.7

91.1/ 7.7/15.4/67.5

99.2/30.2/38.3/93.7

99.5

/

39.6

/

43.3

/

96.4

fire hood

98.9/27.7/35.2/87.9

98.6/23.4/32.2/85.3

70.5/ 0.3/ 2.2/25.3

97.3/27.1/35.3/34.7

91.8/ 3.2/ 9.2/66.7

98.7/25.1/31.3/86.3

99.3

/

38.4

/

42.7

/

93.0

mint

95.0/11.7/23.0/72.3

94.4/ 7.7/18.1/62.3

79.9/ 0.9/ 3.6/43.3

84.1/10.3/22.4/ 9.9

91.1/ 5.7/11.6/64.2

96.5/15.9/27.0/72.6

96.9

/

22.0

/

32.5

/

77.6

mounts

99.3/30.6/37.1/94.9

99.4

/28.0/32.8/95.2

80.5/ 2.2/ 6.8/46.1

94.2/30.0/41.3/43.3

84.3/ 0.4/ 1.1/48.8

99.2/31.4/35.4/93.5

99.4

/

39.9

/

44.3

/

95.6

pcb

97.5/15.8/24.3/88.3

97.0/18.5/28.1/81.6

78.0/ 1.4/ 4.3/41.3

97.2/37.1/40.4/48.8

92.0/ 3.7/ 7.4/66.5

99.2/46.3/50.4/93.1

99.3

/

55.0

/

56.3

/

95.7

phone battery

77.3/22.6/31.7/94.5

85.5/11.2/21.6/88.5

43.4/ 0.1/ 0.9/11.8

79.5/25.6/33.8/39.5

96.8/ 5.3/11.4/85.4

99.4/36.3/41.3/95.3

99.7

/

51.6

/

54.2

/

96.8

phone battery

77.3/22.6/31.7/94.5

85.5/11.2/21.6/88.5

43.4/ 0.1/ 0.9/11.8

79.5/25.6/33.8/39.5

96.8/5.3/11.4/85.4

99.4/36.3/41.3/95.3

99.7

/

51.6

/

54.2

/

96.8

plastic nut

98.8/21.1/29.6/91.0

98.4/20.6/27.1/88.9

77.4/ 0.6/ 3.6/41.5

96.5/44.8/45.7/38.4

81.1/ 0.4/ 3.4/38.6

99.4/33.1/37.3/96.1

99.7

/

41.0

/

45.0

/

97.4

plastic plug

99.1/20.5/28.4/94.9

98.6/17.4/26.1/90.3

78.6/ 0.7/ 1.9/38.8

91.9/20.1/27.3/21.0

92.9/ 8.7/15.0/66.1

99.0/24.2/31.7/91.5

99.4

/

31.7

/

37.2

/

96.4

porcelain doll

99.2/24.8/34.6/95.7

98.7/14.1/24.5/93.2

81.8/ 2.0/ 6.4/47.0

93.1/35.9/40.3/24.8

93.1/ 1.4/ 4.8/70.4

99.2/

31.3

/

36.6

/95.4

99.3

/27.9/33.9/

96.0

regulator

98.0/7.8/16.1/88.6

95.5/9.1/17.4/76.1

76.6/0.1/0.6/38.1

88.8/18.9/23.6/17.5

84.2/0.4/1.5/44.4

97.6/20.6/29.8/87.0

99.3

/

42.2

/

48.9

/

95.6

rolled strip base

99.7

/31.4/39.9/98.4

99.6/20.7/32.2/97.8

80.5/ 1.7/ 5.1/52.1

99.2/

48.7

/

50.1

/55.5

87.7/ 0.6/ 3.2/63.4

99.7/37.4/42.5/98.8

99.7

/41.6/45.5/

98.5

sim card set

98.5/40.2/44.2/89.5

97.9/31.6/39.8/85.0

71.0/ 6.8/14.3/30.8

99.1

/

65.5

/

62.1

/73.9

89.9/ 1.7/ 5.8/60.4

98.8/51.1/50.6/89.4

99.0/52.1/52.9/

90.9

switch

94.4/18.9/26.6/90.9

98.1/33.8/40.6/90.7

71.7/ 3.7/ 9.3/44.2

97.4/57.6/55.6/44.7

90.5/ 1.4/ 5.3/64.2

98.2

/39.9/45.4/92.9

96.7/

62.3

/

63.6

/

95.9

tape

99.7/42.4/47.8/98.4

99.7/29.2/36.9/97.5

77.5/ 1.2/ 3.9/41.4

99.0/61.7/57.6/48.2

81.7/ 0.4/ 2.7/47.3

99.8

/47.1/48.2/98.0

99.8

/

54.0

/

55.8

/98.8

terminalblock

99.5/27.4/35.8/97.6

99.2/23.1/30.5/94.4

87.0/ 0.8/ 3.6/54.8

96.6/40.6/44.1/34.8

75.5/ 0.1/ 1.1/38.5

99.8

/35.3/39.7/98.2

99.8

/

48.0

/

50.7

/

98.8

toothbrush

96.9/26.1/34.2/88.7

95.7/16.4/25.3/84.3

84.7/ 7.2/14.8/52.6

94.3/30.0/37.3/42.8

82.0/ 1.9/ 6.6/54.5

97.5

/27.8/36.7/

91.4

96.9/

38.3

/

43.9

/90.4

toy

95.2/ 5.1/12.8/82.3

93.4/ 4.6/12.4/70.5

67.7/ 0.1/ 0.4/25.0

86.3/ 8.1/15.9/16.4

82.1/ 1.1/ 4.2/50.3

96.0

/16.4/25.8/86.3

94.9/

22.5

/

32.1

/

91.0

toy brick

96.4/16.0/24.6/75.3

97.4

/17.1/27.6/

81.3

86.5/ 5.2/11.1/56.3

94.7/24.6/30.8/45.5

93.5/ 3.1/ 8.1/66.4

96.6/18.0/25.8/74.7

96.8/

27.9

/

34.0

/76.6

transistor1

99.1/29.6/35.5/95.1

98.9/25.6/33.2/94.3

71.7/ 5.1/11.3/35.3

97.3/43.8/44.5/45.4

88.6/ 7.2/15.3/58.1

99.4/39.4/40.0/96.5

99.6

/

53.5

/

53.3

/

97.8

u block

99.6/40.5/45.2/96.9

99.3/22.3/29.6/94.3

76.2/ 4.8/12.2/34.0

96.9/

57.1

/

55.7

/38.5

88.8/ 1.6/ 5.4/54.2

99.5

/37.8/46.1/95.4

99.5

/41.8/45.6/

96.8

usb

98.1/26.4/35.2/91.0

97.9/20.6/31.7/85.3

81.1/ 1.5/ 4.9/52.4

98.4/42.2/47.7/57.1

78.0/ 1.0/ 3.1/28.0

99.2

/39.1/44.4/95.2

99.2

/

45.0

/

48.7

/

97.5

usb adaptor

94.5/ 9.8/17.9/73.1

96.6/10.5/19.0/78.4

67.9/ 0.2/ 1.3/28.9

94.9/

25.5

/

34.9

/36.4

94.0/ 2.3/ 6.6/75.5

97.3/15.3/22.6/82.5

98.7

/23.7/32.7/

91.0

vcpill

98.3/43.1/48.6/88.7

99.1/40.7/43.0/91.3

68.2/ 1.1/ 3.3/22.0

97.1/64.7/62.3/42.3

90.2/ 1.3/ 5.2/60.8

98.7/50.2/54.5/89.3

99.1

/

66.4

/

66.7

/

93.7

wooden beads

98.0/27.1/34.7/85.7

97.6/16.5/23.6/84.6

68.1/

2.4/

6.0/28.3

94.7/38.9/42.9/39.4

85.0/

1.1/

4.7/45.6

98.0/32.6/39.8/84.5

99.1

/

45.8

/

50.1

/

90.5

woodstick

97.8/30.7/38.4/85.0

94.0/36.2/44.3/77.2

76.1/ 1.4/ 6.0/32.0

97.9/

60.3

/

60.0

/51.0

90.9/ 2.6/ 8.0/60.7

97.7/40.1/44.9/82.7

99.0

/50.9/52.1/

90.4

zipper

99.1/44.7/50.2/96.3

98.4/32.5/36.1/95.1

89.9/23.3/31.2/55.5

98.2/35.3/39.0/78.5

90.2/12.5/18.8/53.5

99.3/58.2/61.3/97.6

99.3

/

67.2

/

66.5

/

97.8

Mean

97.3/25.0/32.7/89.6

97.3/21.1/29.2/86.7

75.7/ 2.8/ 6.5/39.0

94.6/37.9/41.7/40.6

88.0/ 2.9/ 7.1/58.1

98.5/33.0/38.7/90.5

98.8

/

42.8

/

47.1

/

93.9

image

ano.

GT

ano.

map

image

S00

500

ano.

GT

ano.

map

image

ano.

GT

ano.

map

image

ano.

GT

ano.

map

image

ano.

GT

ano.

map

image

ano.

GT

ano.

map

image

ano.

GT

ano.

map

image

ano.

GT

ano.

map

image

ano.

GT

ano.

map

image

ano.

GT

ano.

map

image

ano.

GT

ano.

CBR

map

image

ano.

GT

ano.

map

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

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616

617

618

619

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621

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623

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647

NeurIPS Paper Checklist

The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and precede the (optional) supplemental material. The checklist does NOT count towards the page limit.

Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:

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Delete this instruction block, but keep the section heading 'NeurIPS paper checklist" ,

Keep the checklist subsection headings, questions/answers and guidelines below.

Do not modify the questions and only use the provided macros for your answers .

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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Well reflected.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

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It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: Presented in Appendix.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

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While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

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Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

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Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental Result Reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

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Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

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Justification: Yes.

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The answer NA means that the paper does not include experiments.

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Guidelines:

The answer NA means that the paper poses no such risks.

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Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper \"Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection\" introduces a minimalist reconstruction-based framework for unsupervised anomaly detection (UAD) in multi-class settings. The framework focuses on four main components: Foundation Transformers, Noisy Bottleneck, Linear Attention, and Loose Reconstruction. Extensive experiments on MVTec-AD, VisA, and Real-IAD datasets show that Dinomaly achieves superior performance compared to state-of-the-art multi-class and even some class-separated UAD methods.\n\nSTRENGTHS:\n1. Using simple components like Foundation Transformers, Noisy Bottleneck, Linear Attention, and Loose Reconstruction to achieve superior performance is highly original. This is a significant departure from traditional methods that rely on complex designs and multiple modules. It challenges the conventional views: more complex architectures are necessary for better performance in anomaly detection tasks.\n2. The methodology is well-detailed, and the experimental design is robust. The authors conduct extensive experiments on three well-known datasets (MVTec-AD, VisA, and Real-IAD), providing comprehensive performance metrics and comparisons with SOTA methods. The result is convincing, showing that Dinomaly not only outperforms existing MUAD methods but also surpasses some of the best class-separated UAD methods. \n3. The paper is generally clear, well-organized, and relatively reproducible.\n4. The significance of this work is substantial, and makes a valuable contribution to anomaly detection, as it addresses a major challenge in UAD—achieving high performance in multi-class settings without resorting to complex, specialized architectures, and is potentially scalable.\n\nWEAKNESSES:\n1. The paper provides a detailed explanation of the proposed framework but lacks important justification and discussion, it is difficult for readers to realize the novelty and improvements brought by Dinomaly. The author may need to compare Dinomaly to specific previous methods, highlighting the differences and improvements. Discuss how the minimalist approach contrasts with more complex architectures and why this improvement is significant.\n2. The motivations for choosing Noisy Bottleneck and Loose Reconstruction are not deeply explored. For instance, explain in more detail why Noisy Bottleneck helps prevent identity mapping.\n3. The paper claims simplicity but there was no discussion of parameter number, computational complexity, or time complexity in the experiment. \n4. In Loose Constraint, the author claims that 1-group LC mixes low-level and high-level features which is harmful for anomaly localization. How to group the features into the low-semantic-level group and high-semantic-level group in 2-group LC?\n\nQUESTIONS:\n1. Can you provide more detailed justifications for the choice of Noisy Bottleneck and Loose Reconstruction? Why do these help in preventing identity mapping and improving anomaly detection performance?\nSuggestion: A detailed theoretical explanation or additional references would clarify the rationale of the design of Dinomaly and strengthen the argument for its effectiveness.\n2. Provide a more explicit comparison to recent methods in terms of performance and conceptual differences. Highlight any specific limitations of prior work that Dinomaly addresses.\n3. Increase discussion on simplicity and scalability.\n4. The credibility of the paper may benefit from deeper experimental analysis. Known that Dinomaly surpasses compared methods by a large margin on all datasets and all metrics, if its performance is limited under certain conditions, such as when the input is video?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on the Multi-class Unsupervised Anomaly Detection task and proposes a minimalistic reconstruction-based anomaly detection framework — Dinomaly that consists of only vanilla Transformer blocks. In this framework, four key components (Foundation Transformers, Noisy Bottleneck, Linear Attention, and Loose Reconstruction) are introduced to alleviate the performance gap between multi-class and class-separated models. The paper conducts extensive experiments on three major datasets: MVTec-AD, VisA, and Real-IAD. Results show that Dinomaly outperforms current state-of-the-art methods.\n\nSTRENGTHS:\n(1)The paper is well-written and has clear statements which make it easy to understand. \n\n(2)The design of Dinomaly is straightforward but innovative. The use of foundation transformers, noisy bottleneck, linear attention, and loose reconstruction is well-justified. \n\n(3)The paper generally outperformed existing SOTA methods and did enough experiments and comparisons.\n\nWEAKNESSES:\n(1)The method relies heavily on transformer architectures, which might limit its applicability to other types of models.\n\n(2)Transformers can be resource-intensive, and the paper does not fully address the computational cost of training and inference.\n\n(3)The method's generalization to other domains or types of anomaly detection is not fully explored.\n\nQUESTIONS:\n(1) Please specify the computational overhead of your work.\n\n(2) There have been various recent works that attempt to perform anomaly detection in a more general zero-shot/few-shot setting, where the model trained on multiple classes is used to test samples from unseen classes. A discussion of these recent works and how the zero-shot (cross-dataset) performance of this approach should be added. e.g.,\n\n\"WinCLIP: Zero-/few-shot anomaly classification and segmentation\" CVPR'2023\n\n\"AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection\" ICLR'2024\n\n\"Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts\" CVPR'2024\n\n\"PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection\" CVPR'2024\n\n(3) If I understand correctly, the best and second-best results in each table should be highlighted, with the best results in bold and the second-best results underlined. However, in Table 2, the best results for MVTec-AD (P-AP) and VisA (P-AP) are incorrectly marked. Additionally, could you clarify why the Dinomaly (MUAD) model outperforms the Dinomaly (class-separated) model in this metric?\n\nLIMITATIONS:\nLimitations are discussed in Supplementary Sec. A.4.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Dinomaly, a simple yet effective anomaly detection framework using pure Transformer architectures. It identifies four key components essential for multi-class anomaly detection: Foundation Transformers, Noisy Bottleneck, Linear Attention, and Loose Reconstruction. Extensive experiments on MVTec-AD, VisA, and Real-IAD datasets show that Dinomaly achieves superior performance, surpassing both state-of-the-art multi-class and class-separated anomaly detection methods.\n\nSTRENGTHS:\n1. The authors have conducted extensive experiments to validate the effectiveness of their method across multiple anomaly detection tasks.\n2. The authors have proposed a simple yet effective framework that approaches and even surpasses the results of state-of-the-art methods in single-class anomaly detection tasks.\n\nWEAKNESSES:\n1. L53-55 'In addition, previous...': The authors should provide relevant evidence rather than subjective assumptions.\n2. L77: Placing the Related Works section in the appendix is unconventional.\n3. The first component proposed by the authors, Foundation Transformers, was already introduced in the ViTAD paper, which diminishes the overall contribution of the paper. \n4. The input resolution used in the authors' experiments is 448x448, while other comparison methods use 256x256 or 224x224. This is an extremely unfair comparison. Please include results with a 256 resolution in table for a fair comparison.\n5. In the proposed Loose Loss, 90% of the feature points were selected. How was this 90% hyperparameter determined? Please provide ablation study results.\n6. The authors have employed Linear Attention to reduce computational load while maintaining similar performance. It is recommended that the authors compare the parameter count and FLOPs of their method with those of the baseline methods to demonstrate its efficiency. Additionally, it is suggested to conduct ablation studies to verify the computational efficiency of Linear Attention. \n7. Other methods, such as RD4AD, SimpleNet, and UniAD, perform under the proposed settings. The authors can conduct a more equitable comparison.\n\nQUESTIONS:\nSee the Weaknesses.\n\n=== After Rebuttal ===\nI decide to raise my score from 4 to 5.\n\nLIMITATIONS:\nIt is recommended that the authors evaluate the performance of different methods under fair settings.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

comments: SUMMARY:
This paper introduces Dinomaly, a minimalistic unsupervised anomaly detection (UAD) method designed to bridge the performance gap between multi-class UAD and class-separated UAD. Utilizing pure Transformer architectures with key components such as Foundation Transformers, Noisy Bottleneck, Linear Attention, and Loose Reconstruction, Dinomaly achieves superior performance on MVTec-AD, VisA, and Real-IAD benchmarks, surpassing state-of-the-art methods.

STRENGTHS:
1. Dinomaly effectively bridges the performance gap between multi-class and class-separated UAD, achieving superior results on popular benchmarks such as MVTec-AD, VisA, and Real-IAD. 
2.  It utilizes a simple, straightforward approach with pure Transformer architectures, avoiding complex modules or specialized tricks.
3. The detailed ablation study demonstrates the effectiveness of each component—Noisy Bottleneck, Linear Attention, Loose Constraint, and Loose Loss—in enhancing anomaly detection.

WEAKNESSES:
1. The method might be perceived as too application-oriented, lacking broader theoretical contributions.

QUESTIONS:
See the weaknesses.

LIMITATIONS:
See the weaknesses.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-16

comments: SUMMARY:
Dinomaly simplifies the anomaly detection process by eliminating the need for complex designs, additional modules, or specialized techniques. It relies solely on basic Transformer components such as self attention mechanisms and multi-layer perceptrons (MLPs) to perform anomaly detection for multi class images.

STRENGTHS:
This paper has a clear motivation and contribution. The paper effectively proposes the viewpoint of "less is more" in multi class unsupervised anomaly detection, emphasizing how the simplicity of model architecture can achieve or surpass the performance of more complex systems.

WEAKNESSES:
I hope to provide a specific explanation of the information provided by the decision-making process for identifying anomalies in the model.

QUESTIONS:
Is the viewpoint presented in this article too sharp? Large scale models may not necessarily lack their advantages. I acknowledge this work and would like to see other people's questions and the author's response to determine the final score.

LIMITATIONS:
The author candidly acknowledged the limitations of the work and provided the problems that need to be addressed.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 4
DATE: 2024-07-16