Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows

• URL: http://arxiv.org/abs/2008.12577v1
• Date: Fri, 28 Aug 2020 10:49:28 GMT
• Title: Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
• Authors: Marco Rudolph and Bastian Wandt and Bodo Rosenhahn
• Abstract summary: We propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density. Based on these likelihoods we develop a scoring function that indicates defects. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.
• Score: 24.734388664558708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.

Related papers

• Unsupervised Visual Defect Detection with Score-Based Generative Model [17.610722842950555]
  We focus on the unsupervised visual defect detection and localization tasks. We propose a novel framework based on the recent score-based generative models. We evaluate our method on several datasets to demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2022-11-29T11:06:29Z)
• Deep Convolutional Pooling Transformer for Deepfake Detection [54.10864860009834]
  We propose a deep convolutional Transformer to incorporate decisive image features both locally and globally. Specifically, we apply convolutional pooling and re-attention to enrich the extracted features and enhance efficacy. The proposed solution consistently outperforms several state-of-the-art baselines on both within- and cross-dataset experiments.
  arXiv  Detail & Related papers  (2022-09-12T15:05:41Z)
• Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors [15.842163335920954]
  We propose a model based on Retina drilling defects in X-ray images of turbine blades. The application is challenging due to the image resolutions in which defects are very small and hardly captured by the commonly used anchor sizes. We validate the model with $3$-fold cross-validation, showing a very high accuracy in identifying images with defects.
  arXiv  Detail & Related papers  (2022-08-08T18:44:51Z)
• 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)
• One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning [5.370848116287344]
  We propose a one-stage neural network model that can generate high-quality edge images without postprocessing. The proposed model adopts a classic encoder-decoder framework in which a pre-trained neural model is used as the encoder. We propose a new loss function that addresses the pixel-level imbalance in the edge image.
  arXiv  Detail & Related papers  (2022-03-17T15:26:00Z)
• Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection [24.0966076588569]
  We tackle the problem of automatic defect detection without requiring any image samples of defective parts. We propose a novel fully convolutional cross-scale normalizing flow (CS-Flow) that jointly processes multiple feature maps of different scales. Our work sets a new state-of-the-art in image-level defect detection on the benchmark datasets Magnetic Tile Defects and MVTec AD showing a 100% AUROC on 4 out of 15 classes.
  arXiv  Detail & Related papers  (2021-10-06T15:35:13Z)
• Low-Light Image Enhancement with Normalizing Flow [92.52290821418778]
  In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution. The experimental results on the existing benchmark datasets show our method achieves better quantitative and qualitative results, obtaining better-exposed illumination, less noise and artifact, and richer colors.
  arXiv  Detail & Related papers  (2021-09-13T12:45:08Z)
• Improving robustness against common corruptions with frequency biased models [112.65717928060195]
  unseen image corruptions can cause a surprisingly large drop in performance. Image corruption types have different characteristics in the frequency spectrum and would benefit from a targeted type of data augmentation. We propose a new regularization scheme that minimizes the total variation (TV) of convolution feature-maps to increase high-frequency robustness.
  arXiv  Detail & Related papers  (2021-03-30T10:44:50Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)
• Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation [4.626338154327536]
  In industrial inspection tasks, it is common to capture abundant defect-free image samples but very limited anomalous ones. This paper tackles the challenging few-shot defect segmentation task with sufficient normal (defect-free) training images but very few anomalous ones. We present two effective regularization techniques via incorporating abundant defect-free images into the training of a UNet-like encoder-decoder defect segmentation network.
  arXiv  Detail & Related papers  (2020-07-18T14:15:42Z)
• Why Normalizing Flows Fail to Detect Out-of-Distribution Data [51.552870594221865]
  Normalizing flows fail to distinguish between in- and out-of-distribution data. We demonstrate that flows learn local pixel correlations and generic image-to-latent-space transformations. We show that by modifying the architecture of flow coupling layers we can bias the flow towards learning the semantic structure of the target data.
  arXiv  Detail & Related papers  (2020-06-15T17:00:01Z)

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.