RcAE：递归重建框架用于无监督工业异常检测
RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection
AI总结：
RcAE通过递归重建框架有效检测工业异常，结合CRD模块和DPN网络，在参数更少的情况下实现与扩散模型相当的性能。
摘要：
无监督工业异常检测需要在没有标记数据的情况下准确识别缺陷。传统基于自编码器的方法往往在异常抑制不完整和细节丢失方面存在问题，因为其单次解码无法有效处理不同严重程度和规模的异常。我们提出了一种递归架构的自编码器（RcAE），通过迭代重建逐步抑制异常并细化正常结构。与传统单次通过模型不同，这种递归设计自然产生一系列重建，逐步暴露被抑制的异常模式。为了利用这种重建动态，我们引入了交叉递归检测（CRD）模块，该模块在递归步骤中跟踪不一致，增强对细微和大规模异常的检测。此外，我们还集成了细节保持网络（DPN）以恢复通常在重建过程中丢失的高频纹理。广泛的实验表明，我们的方法在现有非扩散方法中表现显著优于现有非扩散方法，并且在参数仅为最近扩散模型的10%的情况下，实现了与之相当的性能，同时推理速度大幅提高。这些结果突显了我们的方法在实际应用中的实用性和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
