CAST is a framework for generating anomalous time series that addresses the scarcity and heterogeneity of anomaly data. It uses a two‑stage approach: pretraining on abundant normal data to learn system dynamics, then finetuning with anomaly structure representations to capture diverse anomaly morphologies. Experiments on real‑world datasets show that CAST outperforms existing methods in both generation quality and downstream task performance.
By Haochen Zhang, Jie Peng, Songyuan Sui, Yu-Chao Huang, Xiangqi Zhu, Tianlong Chen
The paper introduces DPA, a diffusion-based framework that decouples product-agnostic anomaly representations to enable zero-shot anomaly generation. By reusing real anomalies from existing source products and filtering them for plausibility, DPA learns product-irrelevant anomaly embeddings that can be transferred across products. An adaptive mask-guided pipeline and a training-free labeling module further refine the realism and localization of generated anomalies, leading to improved performance on MVTec-AD, VisA, and a new anomaly-transfer benchmark.
By Hang Yao, Yansheng Fu, Ming Liu, Zifei Yan, Yanli Ji, Hongzhi Zhang, Wangmeng Zuo
arXiv:2609.31470v1 Announce Type: new
Abstract: Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ signifi...
By Haixiang Sun, Andrew L. Liu
The paper introduces a variational template matching framework for anomaly detection in patterned structures, representing anomaly templates as transformed instances and using normalized cross‑correlation across the transformation space. It enhances robustness by adding a density‑based statistical anomaly score derived from local intensity distributions via kernel density estimation, which captures distributional concentration and tail behavior more effectively than histogram methods. The structural and statistical cues are fused in a unified formulation, and experiments on biological cell images show the method outperforms classical baselines and rivals ResNet‑50 while remaining fully training‑free and providing explicit localization.
By Qinwu Xu, Yifan Jiang
Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such data is often restricted by privacy concerns and data-sharing constraints, motivating growing interest in synthetic energy data generation.
arXiv:2412.03044v3 Announce Type: replace
Abstract: Video anomaly detection (VAD) is a vital yet complex open-set task in computer vision, commonly tackled through reconstruction-based methods. Howev...
By Xiaofeng Tan, Hongsong Wang, Xin Geng, Liang Wang
arXiv:2609.37314v1 Announce Type: new
Abstract: Synthetic anomaly generation helps expand industrial anomaly datasets when real defects are scarce or unavailable. Existing approaches lie at two extre...
By Abhay Kumar Das, Rajesh Gangireddy, Ashwin Vaidya, Samet Akcay
arXiv:2608. 13932v1 Announce Type: new Abstract: Iterative Generative Models (IGMs) span autoregressive and diffusion paradigms, and hybrid variants that couple them can achieve remarkable image-generation fidelity.
By Jing Gao, Junyi Wu, Wei Wang, Yan Yan, Yao Zhao
arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.
By PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das
arXiv:2608. 03087v1 Announce Type: cross Abstract: Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment.
By Lin Jiang, Dahai Yu, Ravikumar Gelli, Guang Wang
arXiv:2508. 00909v2 Announce Type: replace Abstract: Time series anomaly detection plays a critical role in a wide range of real-world applications.
By Aitor S\'anchez-Ferrera, Usue Mori, Borja Calvo, Jose A. Lozano
arXiv:2606. 07660v1 Announce Type: cross Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations.
By Qiaoyu Chen, Bing Zhang