A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection

• URL: http://arxiv.org/abs/2104.14535v1
• Date: Thu, 29 Apr 2021 17:49:48 GMT
• Title: A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection
• Authors: Shelly Sheynin, Sagie Benaim and Lior Wolf
• Abstract summary: We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions.
• Score: 93.38607559281601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection, the task of identifying unusual samples in data, often relies on a large set of training samples. In this work, we consider the setting of few-shot anomaly detection in images, where only a few images are given at training. We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. We further enhance the representation of our model by using image transformations and optimize scale-specific patch-discriminators to distinguish between real and fake patches of the image, as well as between different transformations applied to those patches. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions. We demonstrate the superiority of our method on both the one-shot and few-shot settings, on the datasets of Paris, CIFAR10, MNIST and FashionMNIST as well as in the setting of defect detection on MVTec. In all cases, our method outperforms the recent baseline methods.

Related papers

• Masked Images Are Counterfactual Samples for Robust Fine-tuning [77.82348472169335]
  Fine-tuning deep learning models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. We propose a novel fine-tuning method, which uses masked images as counterfactual samples that help improve the robustness of the fine-tuning model.
  arXiv  Detail & Related papers  (2023-03-06T11:51:28Z)
• Masked Transformer for image Anomaly Localization [14.455765147827345]
  We propose a new model for image anomaly detection based on Vision Transformer architecture with patch masking. We show that multi-resolution patches and their collective embeddings provide a large improvement in the model's performance. The proposed model has been tested on popular anomaly detection datasets such as MVTec and head CT.
  arXiv  Detail & Related papers  (2022-10-27T15:30:48Z)
• FewGAN: Generating from the Joint Distribution of a Few Images [95.6635227371479]
  We introduce FewGAN, a generative model for generating novel, high-quality and diverse images. FewGAN is a hierarchical patch-GAN that applies quantization at the first coarse scale, followed by a pyramid of residual fully convolutional GANs at finer scales. In an extensive set of experiments, it is shown that FewGAN outperforms baselines both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2022-07-18T07:11:28Z)
• Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification [5.6512908295414]
  We propose an unsupervised domain adaptation approach that uses graph neural networks and, disentangled semantic and domain invariant structural features. We test the proposed method for classification on two challenging medical image datasets with distribution shifts. Experiments show our method achieves state-of-the-art results compared to other domain adaptation methods.
  arXiv  Detail & Related papers  (2022-06-27T09:02:16Z)
• Deep Learning-Based Defect Classification and Detection in SEM Images [1.9206693386750882]
  In particular, we train RetinaNet models using different ResNet, VGGNet architectures as backbone. We propose a preference-based ensemble strategy to combine the output predictions from different models in order to achieve better performance on classification and detection of defects.
  arXiv  Detail & Related papers  (2022-06-20T16:34:11Z)
• PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization [57.37911115888587]
  We introduce a regularizer for the variational modeling of inverse problems in imaging based on normalizing flows. Our regularizer, called patchNR, involves a normalizing flow learned on patches of very few images.
  arXiv  Detail & Related papers  (2022-05-24T12:14:26Z)
• Inpainting Transformer for Anomaly Detection [0.0]
  Inpainting Transformer (InTra) is trained to inpaint covered patches in a large sequence of image patches. InTra achieves better than state-of-the-art results on the MVTec AD dataset for detection and localization.
  arXiv  Detail & Related papers  (2021-04-28T17:27:44Z)
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization [59.719925639875036]
  We propose a framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects.
  arXiv  Detail & Related papers  (2021-04-08T19:04:55Z)
• Semantic Change Detection with Asymmetric Siamese Networks [71.28665116793138]
  Given two aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem is vital in many earth vision related tasks, such as precise urban planning and natural resource management. We present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures.
  arXiv  Detail & Related papers  (2020-10-12T13:26:30Z)
• Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation [18.870983535180457]
  We propose Transformation Consistency Regularization, which delves into a more challenging setting of image-to-image translation. We evaluate the efficacy of our algorithm on three different applications: image colorization, denoising and super-resolution. Our method is significantly data efficient, requiring only around 10 - 20% of labeled samples to achieve similar image reconstructions to its fully-supervised counterpart.
  arXiv  Detail & Related papers  (2020-07-15T17:41:35Z)

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.