Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection

• URL: http://arxiv.org/abs/2107.13118v1
• Date: Wed, 28 Jul 2021 01:14:32 GMT
• Title: Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
• Authors: Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, Hong Zhou
• Abstract summary: Reconstruction-based methods play an important role in unsupervised anomaly detection in images. In this work, we interpret the reconstruction of an image as a divide-and-assemble procedure. We achieve state-of-the-art performance on the challenging MVTec AD dataset.
• Score: 40.778313918994996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstruction-based methods play an important role in unsupervised anomaly detection in images. Ideally, we expect a perfect reconstruction for normal samples and poor reconstruction for abnormal samples. Since the generalizability of deep neural networks is difficult to control, existing models such as autoencoder do not work well. In this work, we interpret the reconstruction of an image as a divide-and-assemble procedure. Surprisingly, by varying the granularity of division on feature maps, we are able to modulate the reconstruction capability of the model for both normal and abnormal samples. That is, finer granularity leads to better reconstruction, while coarser granularity leads to poorer reconstruction. With proper granularity, the gap between the reconstruction error of normal and abnormal samples can be maximized. The divide-and-assemble framework is implemented by embedding a novel multi-scale block-wise memory module into an autoencoder network. Besides, we introduce adversarial learning and explore the semantic latent representation of the discriminator, which improves the detection of subtle anomaly. We achieve state-of-the-art performance on the challenging MVTec AD dataset. Remarkably, we improve the vanilla autoencoder model by 10.1% in terms of the AUROC score.

Related papers

• Attention-Guided Perturbation for Unsupervised Image Anomaly Detection [39.48326211958042]
  We present a reconstruction framework named Attention-Guided Pertuation Network (AGPNet) It learns to add perturbation noise with an attention mask, for accurate unsupervised anomaly detection. Our framework obtains leading anomaly detection performance under various setups including few-shot, one-class, and multi-class setups.
  arXiv  Detail & Related papers  (2024-08-14T12:12:43Z)
• Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detection [4.742650815342744]
  Unsupervised anomaly detection is of great significance for quality inspection in industrial manufacturing. We propose a multi-feature reconstruction network, MFRNet, using crossed-mask restoration in this paper. Our method is highly competitive with or significantly outperforms other state-of-the-arts on four public available datasets and one self-made dataset.
  arXiv  Detail & Related papers  (2024-04-20T05:13:56Z)
• Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection [38.07468257801517]
  We present a novel unified feature reconstruction-based anomaly detection framework termed RLR. RLR utilizes learnable reference representations to compel the model to learn normal feature patterns explicitly. Evaluation of RLR on the 15-category MVTec-AD dataset and the 12-category VisA dataset shows superior performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-18T08:29:47Z)
• Making Reconstruction-based Method Great Again for Video Anomaly Detection [64.19326819088563]
  Anomaly detection in videos is a significant yet challenging problem. Existing reconstruction-based methods rely on old-fashioned convolutional autoencoders. We propose a new autoencoder model for enhanced consecutive frame reconstruction.
  arXiv  Detail & Related papers  (2023-01-28T01:57:57Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.