mlsanityData to 13 Oct 2026

AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detection

arXiv 2504.04340 ↗First posted 6 Apr 2025cs.CVcited by 1

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Abstract (arXiv)

Anomaly generation is an effective way to mitigate data scarcity for anomaly detection task. Most existing works shine at industrial anomaly generation with multiple specialists or large generative models, rarely generalizing to anomalies in other applications. In this paper, we present AnomalyHybrid, a domain-agnostic framework designed to generate authentic and diverse anomalies simply by combining the reference and target images. AnomalyHybrid is a Generative Adversarial Network(GAN)-based framework having two decoders that integrate the appearance of reference image into the depth and edge structures of target image respectively. With the help of depth decoders, AnomalyHybrid achieves authentic generation especially for the anomalies with depth values changing, such a s protrusion and dent. More, it relaxes the fine granularity structural control of the edge decoder and brings more diversity. Without using annotations, AnomalyHybrid is easily trained with sets of color, depth and edge of same images having different augmentations. Extensive experiments carried on HeliconiusButterfly, MVTecAD and MVTec3D datasets demonstrate that AnomalyHybrid surpasses the GAN-based state-of-the-art on anomaly generation and its downstream anomaly classification, detection and segmentation tasks. On MVTecAD dataset, AnomalyHybrid achieves 2.06/0.32 IS/LPIPS for anomaly generation, 52.6 Acc for anomaly classification with ResNet34, 97.3/72.9 AP for image/pixel-level anomaly detection with a simple UNet.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 31 of its 42 references that are in the map. First posted on arXiv 6 Apr 2025.

Builds on

31 of 42 references.

References · 31

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19 more
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  1. 2026When Point Clouds Outperform Pixels: Rethinking Zero-Shot Multimodal Anomaly Detectioncited by 0