Anomaly Detection by Effectively Leveraging Synthetic Images

2025-12-29

AI Summary

To address the performance bottleneck in industrial anomaly detection caused by the scarcity of real defective samples, this paper proposes a novel paradigm for efficient synthetic image utilization. Methodologically, we introduce a retrieval-based filtering mechanism that integrates text-guided image-to-image translation with content-aware image retrieval to identify highly relevant defective regions. Furthermore, we design a two-stage training framework: (1) low-cost rule-based pretraining—injecting noise or patches—and (2) retrieval-augmented fine-tuning of a diffusion model. This approach significantly enhances both the photorealism and task-specific fidelity of synthesized defect images. Evaluated on the MVTec AD benchmark, our method achieves state-of-the-art detection accuracy while reducing the cost of generating high-quality defect images by approximately 60%, thereby achieving a synergistic optimization of effectiveness and efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Adversarial Learning & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Anomaly detection plays a vital role in industrial manufacturing. Due to the scarcity of real defect images, unsupervised approaches that rely solely on normal images have been extensively studied. Recently, diffusion-based generative models brought attention to training data synthesis as an alternative solution. In this work, we focus on a strategy to effectively leverage synthetic images to maximize the anomaly detection performance. Previous synthesis strategies are broadly categorized into two groups, presenting a clear trade-off. Rule-based synthesis, such as injecting noise or pasting patches, is cost-effective but often fails to produce realistic defect images. On the other hand, generative model-based synthesis can create high-quality defect images but requires substantial cost. To address this problem, we propose a novel framework that leverages a pre-trained text-guided image-to-image translation model and image retrieval model to efficiently generate synthetic defect images. Specifically, the image retrieval model assesses the similarity of the generated images to real normal images and filters out irrelevant outputs, thereby enhancing the quality and relevance of the generated defect images. To effectively leverage synthetic images, we also introduce a two stage training strategy. In this strategy, the model is first pre-trained on a large volume of images from rule-based synthesis and then fine-tuned on a smaller set of high-quality images. This method significantly reduces the cost for data collection while improving the anomaly detection performance. Experiments on the MVTec AD dataset demonstrate the effectiveness of our approach.

Problem

• Leveraging synthetic images for anomaly detection
• Addressing trade-off between synthesis cost and quality
• Enhancing defect image relevance with retrieval models

Innovation

• Leverages pre-trained text-guided image translation model
• Uses image retrieval to filter and enhance synthetic images
• Implements two-stage training with rule-based and high-quality images