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

08/28/2020
by   Marco Rudolph, et al.
0

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.

READ FULL TEXT

page 1

page 5

page 8

research
10/06/2021

Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection

In industrial manufacturing processes, errors frequently occur at unpred...
research
03/22/2022

VQ-Flows: Vector Quantized Local Normalizing Flows

Normalizing flows provide an elegant approach to generative modeling tha...
research
06/15/2020

Why Normalizing Flows Fail to Detect Out-of-Distribution Data

Detecting out-of-distribution (OOD) data is crucial for robust machine l...
research
12/12/2020

Computer Vision and Normalizing Flow Based Defect Detection

Surface defect detection is essential and necessary for controlling the ...
research
02/02/2021

Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement

Individual tooth segmentation and identification from cone-beam computed...
research
05/17/2022

Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training

Accurate and reliable building footprint maps are vital to urban plannin...
research
08/31/2023

Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

Convolutional neural networks (CNNs) have achieved high performance in s...

Please sign up or login with your details

Forgot password? Click here to reset