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REVIEW 5 major objections 5 minor 36 references

Anomaly detection using Diffusion-based methods

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Diffusion-based generative models can serve as strong anomaly detectors by scoring reconstruction error, and the paper claims they beat classical methods like Isolation Forest, One-Class SVM, and COPOD on all tested image datasets.

desk verdict A routine diffusion-anomaly-detection benchmark whose own table contradicts its claim of universal superiority, and whose high-resolution result is an undocumented single number. read the letter →

arxiv 2412.07539 v1 pith:YSUVMJIG submitted 2024-12-10 cs.LG

Aryan Bhosale , Samrat Mukherjee , Biplab Banerjee , Fabio Cuzzolin This is my paper · ORCID
classification cs.LG
keywords anomalydetectiondiffusionmodelsDDPMTransformerreconstructionerrorunsupervisedlearningAUC-ROCscalability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that diffusion-based generative models—models that learn to add noise to data and then reverse the process—can act as strong unsupervised anomaly detectors by scoring how well a test image can be reconstructed after training only on normal images. The authors claim that this reconstruction-error signal outperforms classical detectors such as Isolation Forest, One-Class SVM, and COPOD on all the datasets they test, including small image benchmarks and a high-resolution ImageNet subset. If the claim holds, practitioners could adopt diffusion models as an off-the-shelf choice for industrial defect detection and other settings where anomalies are rare and normal data is plentiful.

What carries the argument

The central object is the reconstruction error of a trained diffusion model. The forward process corrupts an input $x_0$ by $x_t = \sqrt{\bar{\alpha}_t}x_0 + \sqrt{1-\bar{\alpha}_t}\epsilon_t$, and a U-Net (for DDPM) or a Vision Transformer (for DiT) learns to predict the added noise $\epsilon_t$ under the simple objective $\mathcal{L}_{\text{simple}}(\theta) = \|\epsilon_\theta(x_t) - \epsilon_t\|_2^2$. At test time, the model denoises a noisy version of the input, and the magnitude of the difference between the original and the reconstruction serves as the anomaly score: normal inputs reconstruct well, anomalies do not.

What would settle it

Re-run the Mini-ImageNet experiment with the subset composition, model size, number of inference timesteps, and anomaly-score calibration documented, and check whether a DiT-based detector still exceeds OCSVM and IForest in AUC-ROC; also check every row of Table 1 to see whether at least one diffusion variant beats every classical method, since any counterexample row (for instance CIFAR10_7) would refute the 'all datasets' claim as stated.

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Extended reading notes

Core claim

The core discovery is that the gap between a normal image and its diffusion reconstruction is consistently smaller than the gap for an anomalous image, and that this separation is strong enough to rank anomalies more accurately than classical one-class classifiers. The paper reports AUC-ROC numbers for DDPM, Diffusion Transformer (DiT), and Denoising Transformer Embedding (DTE) variants across compact image sets and a Mini-ImageNet subset, and interprets the results as evidence that the iterative denoising trajectory learns a rich model of the normal distribution. In the high-resolution scalability test, the DiT-based detector reaches an AUC of 0.635, ahead of One-Class SVM (0.594) and Isolation Forest (0.584), which the authors read as demonstrating that diffusion models scale to high-dimensional data without the performance plateau seen in traditional methods.

Load-bearing premise

The central claim collapses if the single Mini-ImageNet scalability experiment is not a fair and fully specified test, because the paper gives no details of the subset, model size, inference timesteps, or how the anomaly score was calibrated.

Editorial extensions

If this is right

  • Practitioners can use diffusion-based reconstruction error as an unsupervised anomaly score without needing anomaly labels, only a training set of normal images.
  • The reported gains over Isolation Forest, One-Class SVM, and COPOD suggest diffusion models are a viable drop-in replacement for classical detectors on compact image benchmarks.
  • The Mini-ImageNet result implies that scaling to 224×224 images does not erase the advantage of diffusion methods, pointing to DiT-style architectures for real-world high-resolution deployment.
  • Because reconstruction error is tied to the learned normal distribution, the same training recipe applies across datasets with only the normal class available, supporting domain transfer to industrial and medical imaging.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A re-run of the Mini-ImageNet experiment with a fully documented protocol—subset composition, model size, inference timesteps, and anomaly-score calibration—would settle whether the scalability conclusion generalizes beyond the single reported AUC of 0.635.
  • The 'outperform on all datasets' claim depends on how 'outperform' is defined; checking each row of Table 1 to see whether at least one diffusion variant beats every classical method would clarify whether the claim holds literally.
  • The per-dataset spread among DDPM, DiT, and DTE suggests that the choice of diffusion architecture matters as much as the choice between diffusion and classical methods; selecting the best variant per dataset could change the overall comparison.
  • A natural extension of the reconstruction-error mechanism is to measure how the anomaly score responds to controlled perturbations of normal inputs, which would probe how sharply the learned normal manifold is separated.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper benchmarks reconstruction-based diffusion anomaly detectors (DDPM, DiT, and DTE) against classical baselines (Isolation Forest, OCSVM, COPOD) on several compact image datasets from ADBench (CIFAR-10, MNIST-C, SVHN, FashionMNIST, MVTec-AD) and one high-resolution Mini-ImageNet subset, reporting AUC-ROC scores. The authors claim that diffusion-based methods exhibit superior adaptability, scalability, and robustness, with the Table 1 caption stating that diffusion methods outperform conventional methods on all datasets.

Significance. If the empirical claims were supported, the paper would offer practical evidence that diffusion reconstruction models are a reliable off-the-shelf choice for image anomaly detection, including high-resolution industrial and natural image settings. The paper does provide a multi-dataset comparison and uses a standard metric, which are useful starting points. However, the central claims are internally contradicted by the paper's own table, no uncertainty quantification is provided, and the sole high-resolution result is undocumented. The absence of code, hyperparameters, and a reproducible protocol means the benchmark cannot currently be verified or reused.

major comments (5)
  1. [Section 4.4, Table 1] The caption 'Diffusion-based methods outperform conventional methods on all datasets' is contradicted by the table itself. On CIFAR10_7, OCSVM (67.06) beats DDPM (66.82), DiT (66.30), and DTE (52.21); on MNIST-C_fog, OCSVM (91.09) beats DiT (90.78). Additionally, DTE falls below chance (50%) on many rows, including MVTec-AD_capsule (44.15), MVTec-AD_zipper (49.08), and MVTec-AD_bottle (50.23). The Abstract's claim of 'superior adaptability, scalability, and robustness' is therefore not supported by the reported numbers.
  2. [Section 4.4, Table 1] No error bars, standard deviations, confidence intervals, or significance tests are reported for any AUC value. Since several comparisons are within one point (e.g., CIFAR10_4: DDPM 77.42 vs OCSVM 76.93; MVTec-AD_leather: DDPM 99.38 vs OCSVM 99.39), the differences may be pure noise. The claim of robustness across noisy or adversarial datasets is not statistically established.
  3. [Section 4.3, Figure 1] The high-resolution scalability claim rests entirely on a single reported value, 'Ours = 0.635' for Mini-ImageNet. The paper does not specify which architecture (DDPM, DiT, or DTE) is used, the subset composition, model size, number of diffusion timesteps, the definition of reconstruction error, or how the anomaly score is calibrated. Without these details, this central scalability result is not verifiable.
  4. [Section 4.4, Table 1] COPOD returns exactly 50.00% on 18 of the 28 compact datasets, which strongly suggests either a degenerate anomaly score or a faulty evaluation setup. The paper offers no explanation for this behavior, and this casts doubt on the fairness and validity of the baseline comparison.
  5. [Sections 3.5 and 4.4] The experimental protocol is not reproducible: no training hyperparameters (learning rate, batch size, epochs, optimizer), no architecture details (U-Net depth, DiT patch size), no noise schedule, and no code are provided. Furthermore, the manuscript lists many auxiliary figures (Ablation1.png, Experiment1.png through Experiment15.png, training_protocol.png, vae_intuition_diagram.png) that are never cited or discussed in the body, so any supporting evidence they might contain is not accessible to the reader.
minor comments (5)
  1. [Abstract] There is a typo in the first sentence: 'f or' should be 'for'.
  2. [Section 2.4 and References] The term DTE is never defined, and reference [5] is cited for both DiT and DTE, but [5] is the DDIM paper by Song et al., not a source for Diffusion Transformers or Denoising Transformer Embeddings.
  3. [Section 3.1, Reference [28]] The citation for 'Dickstein et al.' is misattributed: the title 'Deep generative image models using a Laplacian pyramid of adversarial networks' is the Denton et al. paper, whereas the relevant Sohl-Dickstein et al. work is 'Deep Unsupervised Learning using Nonequilibrium Thermodynamics'.
  4. [Section 4.1] FashionMNIST appears in Table 1 (e.g., FashionMNIST_6, FashionMNIST_3, FashionMNIST_9) but is not described in the dataset enumeration in Section 4.1.
  5. [Section 4.2] The paper states that 'We have performed experiments on two categories of datasets' and calls MVTec-AD a 'compact' dataset, but MVTec-AD images are not small-scale (they are typically hundreds of pixels); this categorization is inconsistent with the later claim that Mini-ImageNet is the only high-resolution dataset.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical benchmark against external baselines; its unsupported strong claims are correctness/reproducibility issues, not derivation-circularity.

full rationale

This paper contains no theoretical derivation whose conclusion could reduce to its assumptions, no fitted parameter later renamed as a prediction, and no load-bearing self-citation chain. The only author self-citation (ref. [17], which includes coauthor Fabio Cuzzolin) appears in the related-work discussion of continual semi-supervised learning and is not used to justify the anomaly-detection results. The reconstruction-error premise is attributed to prior external works ([23], [35], [36]) as motivation, not as the basis of a claimed derivation. The central claim that 'Diffusion-based methods outperform conventional methods on all datasets' is an empirical summary, and it is internally contradicted by Table 1 itself (e.g., CIFAR10_7: OCSVM 67.06 vs. DDPM 66.82 and DiT 66.30; MNIST-C_fog: OCSVM 91.09 vs. DiT 90.78). Similarly, the Mini-ImageNet scalability claim rests on a single reported AUC (0.635) with no subset composition, model size, timestep count, or anomaly-score calibration details, and the appended figure listing shows many figures that do not appear in the main text. These are serious evidence-weight, reproducibility, and internal-consistency problems, but they are not circularity: the paper does not define its conclusion in terms of its inputs, does not fit a parameter and then call the fit a prediction, and does not import a uniqueness theorem or ansatz from the authors' own prior work. Under the specified standard, the honest finding is no significant circularity, score 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper makes no theoretical derivation, so the ledger contains only domain assumptions and unreported configuration parameters. No invented entities are introduced; DTE is an undefined label for an existing idea, not a new construct. The main burden is experimental: the central claim rests on unstated assumptions about the validity of reconstruction error, the representativeness of Mini-ImageNet, and the correctness of baseline implementations.

free parameters (2)
  • DDPM/DiT training hyperparameters = not reported
    Number of timesteps, learning rate, batch size, U-Net/DiT depth, and noise schedule are not given; these choices affect all reported AUC values.
  • Number of denoising steps at inference = not reported
    Reconstruction error depends on how many reverse steps are run; without this number, the anomaly scores cannot be reproduced.
assumptions (4)
  • domain assumption Reconstruction error from a diffusion model is a valid anomaly score; normal samples reconstruct with lower error than anomalies.
    Invoked in Section 3.4 and used to score all datasets; the paper cites [23,35,36] but does not validate this assumption for the specific models and datasets.
  • domain assumption The Mini-ImageNet subset is a representative high-resolution scalability benchmark.
    Section 4.3 describes the subset only as 'Mini-ImageNet'; no composition, size, or split details are given, so the scalability claim depends on this unstated assumption.
  • domain assumption The baseline implementations (IForest, OCSVM, COPOD) are correct and comparable.
    COPOD returns exactly 50.00 on many datasets, which is suspicious; the paper assumes the implementation is not broken.
  • domain assumption AUC-ROC on the reported test splits is a sufficient metric for comparing anomaly detectors.
    Section 4.4 uses only AUC-ROC; no statistical significance, confidence intervals, or additional metrics are provided.

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Cite this review

Pith. "Pith review of Anomaly detection using Diffusion-based methods." pith.science (2026). https://pith.science/paper/YSUVMJIG

@misc{pith2026241207539,
  author       = {Pith},
  title        = {Pith review of: Anomaly detection using Diffusion-based methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YSUVMJIG}},
  note         = {Machine review of arXiv:2412.07539}
}
read the original abstract

This paper explores the utility of diffusion-based models for anomaly detection, focusing on their efficacy in identifying deviations in both compact and high-resolution datasets. Diffusion-based architectures, including Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs), are evaluated for their performance using reconstruction objectives. By leveraging the strengths of these models, this study benchmarks their performance against traditional anomaly detection methods such as Isolation Forests, One-Class SVMs, and COPOD. The results demonstrate the superior adaptability, scalability, and robustness of diffusion-based methods in handling complex real-world anomaly detection tasks. Key findings highlight the role of reconstruction error in enhancing detection accuracy and underscore the scalability of these models to high-dimensional datasets. Future directions include optimizing encoder-decoder architectures and exploring multi-modal datasets to further advance diffusion-based anomaly detection.

Figures

Figures reproduced from arXiv: 2412.07539 by the authors.

Figure 1
Figure 1. AUC Scores for Anomaly Detection Methods on the Hig [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗

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Reference graph

Works this paper leans on

36 extracted references · 33 canonical work pages

  1. [4]

    Denoising diffusion implicit models

    Jonathan Ho, Xin Chen, Suraj Srinivas, and Pieter Abbeel . Denoising diffusion implicit models. arXiv preprint arXiv:2006.11239 , 2020

    arXiv 2006

  2. [5]

    Denoisin g diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoisin g diffusion implicit models. arXiv preprint arXiv:2010.02502, 2021

    arXiv 2010

  3. [28]

    Deep generative image models using a laplacian pyram id of adversarial networks

    Jascha Sohl-Dickstein, Steven Weiss, D P Kingma, Shaki r Mohamed, and Ruslan Salakhut- dinov. Deep generative image models using a laplacian pyram id of adversarial networks. Proceedings of the 28th International Conference on Neural Information Processing Systems , pages 829–837, 2015

    work page 2015

  4. [31]

    Dis- criminative unsupervised feature learning with exemplar c onvolutional neural networks

    Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox. Dis- criminative unsupervised feature learning with exemplar c onvolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 38(9):1734–1747, 2020

    work page 2020

  5. [1]

    Isolation f orest

    Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. Isolation f orest. In 2008 Eighth IEEE International Conference on Data Mining , pages 413–422. IEEE, 2008

    work page 2008

  6. [2]

    Estimating the support of a high-dimensional d istribution

    Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Al exander J Smola, and Robert C Williamson. Estimating the support of a high-dimensional d istribution. Neural computation , 13(7):1443–1471, 2001

    work page 2001

  7. [3]

    Copod: Copula-based outlier detection

    Y uxin Li, Shirui Zhao, Davide Botta, Francesco Iorio, an d Alexandros Kalousis. Copod: Copula-based outlier detection. Proceedings of the 29th ACM International Conference on Information & Knowledge Management , pages 2777–2785, 2020

    work page 2020

  8. [6]

    Semi-supervised anomaly detection through self-supervised learning and fe ature refinement

    Alejandro Villa, Diego Fernandez, Sergio Torroba, and M iguel Ferrer. Semi-supervised anomaly detection through self-supervised learning and fe ature refinement. Pattern Recog- nition Letters, 145:272–279, 2021

    work page 2021

Show all 36 references
  1. [7]

    Bagging-randomminer: A one-class classifier for file access-based masquerade detec tion

    José Benito Camiña, Miguel Angel Medina-Pérez, Raúl Mon roy, Octavio Loyola-González, Luis Angel Pereyra Villanueva, and Luis Carlos González Gur rola. Bagging-randomminer: A one-class classifier for file access-based masquerade detec tion. Machine Vision and Applica- tions, 30(...

    2019

  2. [8]

    Deep learning for anomaly detection: A review

    Guansong Pang, Chunhua Shen, Longbing Cao, and Anton V an den Hengel. Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2):1–38, 2021

    2021

  3. [9]

    A unifying review of deep and shallow anomaly detection

    Lukas Ruff, Jan R Kauffmann, Robert A V andermeulen, et al . A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE , 109(5):756–795, 2021

    2021

  4. [10]

    V andermeulen, Nico Görnitz, et al

    Lukas Ruff, Robert A. V andermeulen, Nico Görnitz, et al . Deep one-class classification. In Proceedings of the International Conference on Machine Lea rning, volume 87, pages 4393– 4402, 2018

    2018

  5. [11]

    Deep autoencoding gau ssian mixture model for un- supervised anomaly detection

    Bo Zong, Qi Song, and Hongyu Chen. Deep autoencoding gau ssian mixture model for un- supervised anomaly detection. In Proceedings of the International Conference on Learning Representations, 2018

    2018

  6. [12]

    Contrastive learning for semi-supervised anomaly detection

    Kfir Shenkar, Boaz Nadler, and Shie Mannor. Contrastive learning for semi-supervised anomaly detection. Advances in Neural Information Processing Systems, 35:4624–4636, 2022

    2022

  7. [13]

    Anomaly detection wi th robust deep autoencoders

    Chong Zhou and Randy C Paffenroth. Anomaly detection wi th robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conferenc e on Knowledge Discovery and Data Mining , pages 665–674, 2017

    2017

  8. [14]

    Drocc : Deep robust one-class classifica- tion

    Sachin Goyal, Ujjwal Gupta, and Anshumali Kumar. Drocc : Deep robust one-class classifica- tion. International Conference on Machine Learning , pages 3663–3674, 2020

    2020

  9. [15]

    Classification-based a nomaly detection for general data

    Liron Bergman and Y edid Hoshen. Classification-based a nomaly detection for general data. International Conference on Learning Representations , 2020

    2020

  10. [16]

    Lunar: Unifying local outlier detection methods via graph neural networks

    Adam Goodge, Bryan Hooi, See-Kiong Ng, and Wee-Siong Ng . Lunar: Unifying local outlier detection methods via graph neural networks. In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence , pages 6737–6745. AAAI Press, 2022. doi: 10.1609/aaai.v36i6.20629. 8

    2022 doi

  11. [17]

    International workshop o n continual semi-supervised learning: Introduction, benchmarks and baselines

    Ajmal Shahbaz, Salman Khan, Mohammad Asiful Hossain, V incenzo Lomonaco, Kevin Can- nons, Zhan Xu, and Fabio Cuzzolin. International workshop o n continual semi-supervised learning: Introduction, benchmarks and baselines. arXiv preprint arXiv:2110.14613 , 2021

    arXiv

  12. [18]

    Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learn- ing

    Liyuan Wang, Kuo Y ang, Chongxuan Li, Lanqing Hong, Zhen guo Li, and Jun Zhu. Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learn- ing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pag...

    2021

  13. [19]

    V ariational autoencoder ba sed anomaly detection using recon- struction probability

    Jinwon An and Sungzoon Cho. V ariational autoencoder ba sed anomaly detection using recon- struction probability. Special Lecture on IE , 2(1):1–18, 2015

    2015

  14. [20]

    Anomaly detection us ing autoencoders with nonlinear dimensionality reduction

    Mayu Sakurada and Takehisa Y airi. Anomaly detection us ing autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd W orkshop on Machine Learning for Sensory Data Analysis , pages 4–11. ACM, 2014

    2014

  15. [21]

    Learning from privil eged information for anomaly detec- tion

    Y ahui Xia, Yibing Li, and Fang Liu. Learning from privil eged information for anomaly detec- tion. IEEE Transactions on Neural Networks and Learning Systems , 26(8):1752–1763, 2015

    2015

  16. [22]

    Generative adversarial active learning for unsupervised outlier dete ction

    Weixin Liu, Kai Ming Ting, Jonathan Zhou, Lia Washingto n, and Zhi-Hua Zhang. Generative adversarial active learning for unsupervised outlier dete ction. IEEE Transactions on Knowl- edge and Data Engineering , 31(10):2010–2023, 2019

    2010

  17. [23]

    Diffusion anomaly detection: Leveraging the power of gener ative models

    Johannes Wolleb, Robin Sandkühler, Fabian Bieder, Jo S chlemper, and Daniel Rueckert. Diffusion anomaly detection: Leveraging the power of gener ative models. arXiv preprint arXiv:2206.04593, 2022

    2022 arXiv

  18. [24]

    Diffusion models for anomaly detection and repair

    Han Zhang, Y u Y ang, Dan Wei, Yinglong Wang, and Zhenguo X u. Diffusion models for anomaly detection and repair. International Conference on Learning Representations , 2023

    2023

  19. [25]

    Anoddp m: Anomaly detection with denois- ing diffusion probabilistic models

    Cameron Wyatt, Ellie Schwartz, and Shie Mannor. Anoddp m: Anomaly detection with denois- ing diffusion probabilistic models. Advances in Neural Information Processing Systems , 35: 1236–1248, 2022

    2022

  20. [26]

    Tim e-series anomaly detection using ddpm-based reconstruction

    Craig Graham, Stefan Zickau, and David Strohmeier. Tim e-series anomaly detection using ddpm-based reconstruction. IEEE Transactions on Neural Networks and Learning Systems , 2023

    2023

  21. [27]

    Checkerboard di ffusion models for anomaly detec- tion via image in-painting

    Shiyuan Liu, Liwei Xu, and Junjun Wang. Checkerboard di ffusion models for anomaly detec- tion via image in-painting. arXiv preprint arXiv:2303.06789 , 2023

    2023

  22. [29]

    Denoising di ffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising di ffusion probabilistic models. Ad- vances in Neural Information Processing Systems , 33:6840–6851, 2020

    2020

  23. [30]

    U- net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Interven- tion (MICCAI), 9351:234–241, 2015

    2015

  24. [32]

    Diffusion tra nsformers: Generative models for effi- cient image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion tra nsformers: Generative models for effi- cient image synthesis. arXiv preprint arXiv:2103.05420 , 2021

    2021 arXiv

  25. [33]

    Auto-encoding variational b ayes

    D P Kingma and Max Welling. Auto-encoding variational b ayes. In Proceedings of the 2nd International Conference on Learning Representations (IC LR), 2013. 9

    2013

  26. [34]

    Improved deno ising diffusion probabilistic mod- els

    Alexander Nichol and Prafulla Dhariwal. Improved deno ising diffusion probabilistic mod- els. In Proceedings of the 38th International Conference on Machin e Learning (ICML), pages 8162–8171, 2021

    2021

  27. [35]

    Diad: A diffusion-based fr amework for multi-class anomaly detection, 2023

    Haoyang He, Jiangning Zhang, Hongxu Chen, Xuhai Chen, Z hishan Li, Xu Chen, Y abiao Wang, Chengjie Wang, and Lei Xie. Diad: A diffusion-based fr amework for multi-class anomaly detection, 2023

    2023

  28. [36]

    Ablation1.png

    Victor Livernoche, Vineet Jain, Y ashar Hezaveh, and Siamak Ravanbakhsh. On diffusion mod- eling for anomaly detection. arXiv preprint arXiv:2305.18593 , 2023. 10 This figure "Ablation1.png" is available in "png" format from: http://arxiv.org/ps/2412.07539v1 This figure "Exper...

    2023 arXiv

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