arXiv:2605. 02439v3 Announce Type: replace-cross Abstract: Synthesizing realistic and diverse anomalous samples from limited data is vital for robust model generalization.
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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.
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.
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...
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.
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...