Produce Once, Utilize Twice for Anomaly Detection

• URL: http://arxiv.org/abs/2312.12913v1
• Date: Wed, 20 Dec 2023 10:49:49 GMT
• Title: Produce Once, Utilize Twice for Anomaly Detection
• Authors: Shuyuan Wang, Qi Li, Huiyuan Luo, Chengkan Lv, Zhengtao Zhang
• Abstract summary: We derive POUTA, which improves both the accuracy and efficiency by reusing the discriminant information potential in the reconstructive network. POUTA achieves better performance than the state-of-the-art few-shot anomaly detection methods without any special design.
• Score: 6.501323305130114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual anomaly detection aims at classifying and locating the regions that deviate from the normal appearance. Embedding-based methods and reconstruction-based methods are two main approaches for this task. However, they are either not efficient or not precise enough for the industrial detection. To deal with this problem, we derive POUTA (Produce Once Utilize Twice for Anomaly detection), which improves both the accuracy and efficiency by reusing the discriminant information potential in the reconstructive network. We observe that the encoder and decoder representations of the reconstructive network are able to stand for the features of the original and reconstructed image respectively. And the discrepancies between the symmetric reconstructive representations provides roughly accurate anomaly information. To refine this information, a coarse-to-fine process is proposed in POUTA, which calibrates the semantics of each discriminative layer by the high-level representations and supervision loss. Equipped with the above modules, POUTA is endowed with the ability to provide a more precise anomaly location than the prior arts. Besides, the representation reusage also enables to exclude the feature extraction process in the discriminative network, which reduces the parameters and improves the efficiency. Extensive experiments show that, POUTA is superior or comparable to the prior methods with even less cost. Furthermore, POUTA also achieves better performance than the state-of-the-art few-shot anomaly detection methods without any special design, showing that POUTA has strong ability to learn representations inherent in the training data.

Related papers

• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection [1.0358639819750703]
  In unsupervised anomaly detection (UAD) research, it is necessary to develop a computationally efficient and scalable solution. We revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. We propose Feature Attenuation of Defective Representation (FADeR) that only employs two layers which attenuates feature information of anomaly reconstruction.
  arXiv  Detail & Related papers  (2024-07-05T15:44:53Z)
• A Recover-then-Discriminate Framework for Robust Anomaly Detection [46.32260480448735]
  Anomaly detection (AD) has been extensively studied and applied in a wide range of scenarios in the recent past. There are still gaps between achieved and desirable levels of recognition accuracy for making AD for practical applications. We propose a novel Recover-then-Discriminate (ReDi) framework for AD. ReDi takes a self-generated feature map and a selected prompted image as explicit input information to solve problems in case-1. Concurrently, a feature-level discriminative network is proposed to enhance abnormal differences between the recovered representation and the input representation.
  arXiv  Detail & Related papers  (2024-06-07T03:34:16Z)
• A Hierarchically Feature Reconstructed Autoencoder for Unsupervised Anomaly Detection [8.512184778338806]
  It consists of a well pre-trained encoder to extract hierarchical feature representations and a decoder to reconstruct these intermediate features from the encoder. The anomalies can be detected when the decoder fails to reconstruct features well, and then errors of hierarchical feature reconstruction are aggregated into an anomaly map to achieve anomaly localization. Experiment results show that the proposed method outperforms the state-of-the-art methods on MNIST, Fashion-MNIST, CIFAR-10, and MVTec Anomaly Detection datasets.
  arXiv  Detail & Related papers  (2024-05-15T07:20:27Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection [1.951082473090397]
  We propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow. Compared to mainstream unsupervised approaches, our self-supervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Our approach showcases new state-of-the-art results, achieving a performance of 99.6% in image-level AUROC on the MVTecAD dataset and 96.8% in image-level AUROC on the BTAD dataset.
  arXiv  Detail & Related papers  (2023-11-12T10:07:03Z)
• Benchmarking Deep Models for Salient Object Detection [67.07247772280212]
  We construct a general SALient Object Detection (SALOD) benchmark to conduct a comprehensive comparison among several representative SOD methods. In the above experiments, we find that existing loss functions usually specialized in some metrics but reported inferior results on the others. We propose a novel Edge-Aware (EA) loss that promotes deep networks to learn more discriminative features by integrating both pixel- and image-level supervision signals.
  arXiv  Detail & Related papers  (2022-02-07T03:43:16Z)
• Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection [0.0]
  We propose a novel reconstruction-based model for anomaly detection, called Y-GAN. The model consists of a Y-shaped auto-encoder and represents images in two separate latent spaces.
  arXiv  Detail & Related papers  (2021-09-28T20:17:04Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• BiDet: An Efficient Binarized Object Detector [96.19708396510894]
  We propose a binarized neural network learning method called BiDet for efficient object detection. Our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal. Our method outperforms the state-of-the-art binary neural networks by a sizable margin.
  arXiv  Detail & Related papers  (2020-03-09T08:16:16Z)

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.