RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection

2025-12-12

AI Summary

Unsupervised industrial anomaly detection faces the challenge that single-pass reconstruction struggles to simultaneously suppress anomalies and preserve fine-grained details. To address this, we propose a recursive autoencoder framework that iteratively refines reconstruction for precise anomaly localization. Specifically, we design a Cross-Recursive Detection (CRD) module to model the dynamic evolution of anomalies across recursion steps, introduce a Detail-Preserving Network (DPN) to recover high-frequency textures, and enforce cross-recursive consistency constraints alongside an unsupervised anomaly scoring mechanism. Without relying on diffusion processes, our method achieves performance competitive with state-of-the-art diffusion-based models—while using only 10% of their parameters and significantly accelerating inference. Extensive experiments demonstrate substantial improvements over non-diffusion-based SOTA methods on benchmark industrial datasets, including MVTec-AD.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying severity and scale. We propose a recursive architecture for autoencoder (RcAE), which performs reconstruction iteratively to progressively suppress anomalies while refining normal structures. Unlike traditional single-pass models, this recursive design naturally produces a sequence of reconstructions, progressively exposing suppressed abnormal patterns. To leverage this reconstruction dynamics, we introduce a Cross Recursion Detection (CRD) module that tracks inconsistencies across recursion steps, enhancing detection of both subtle and large-scale anomalies. Additionally, we incorporate a Detail Preservation Network (DPN) to recover high-frequency textures typically lost during reconstruction. Extensive experiments demonstrate that our method significantly outperforms existing non-diffusion methods, and achieves performance on par with recent diffusion models with only 10% of their parameters and offering substantially faster inference. These results highlight the practicality and efficiency of our approach for real-world applications.

Problem

• Unsupervised anomaly detection without labeled data
• Suppressing anomalies while preserving fine details
• Tracking inconsistencies across recursive reconstruction steps

Innovation

• Recursive autoencoder architecture for progressive anomaly suppression
• Cross Recursion Detection module tracks inconsistencies across reconstruction steps
• Detail Preservation Network recovers high-frequency textures lost in reconstruction