Official Review · ICLR 2024

In this paper, the authors investigate diffusion modeling for anomaly detection and introduce an efficient approach called Diffusion Time Estimation (DTE), showing its competitive performance and improved inference times compared to traditional methods and deep learning techniques. 1. This paper is well-organized and easy to follow. 2. Investigating the diffusion model to facilitate anomaly detection is an interesting and promising research issue. 3. The authors conduct comprehensive experiments to demonstrate the effectiveness of the proposed method. 1. The contribution of this paper appears limited. There are several existing works on anomaly detection utilizing diffusion models, such as those mentioned below: [1] Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model, ICCV23. [2] Feature Prediction Diffusion Model for Video Anomaly Detection, ICCV23. [3] DiffusionAD: Denoising Diffusion for Anomaly Detection, Arxiv. [4] Diffusion models for medical anomaly detection, MCCAI22. Can the authors point out the unique contributions of this paper compared with them? 2. The motivation and rationale for introducing the diffusion model into anomaly detection are somewhat unclear. The authors should emphasize it. 3. Authors should compare with some latest AD methods proposed in 2023. Besides, it would be more convincing to include diffusion model-based anomaly detection approaches. 4. The reviewer has thoroughly reviewed the experimental results presented in the appendix. The authors have done a comprehensive experiment by including the results from dozens of datasets in this paper. However, the reviewer observed that the proposed method did not consistently outperform other methods on each individual dataset. It achieved the best performance only on a subset of the datasets. See weakness.