English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The detection of manufacturing errors is crucial in fabrication processes toensure product quality and safety standards. Since many defects occur veryrarely and their characteristics are mostly unknown a priori, their detectionis still an open research question. To this end, we propose DifferNet: Itleverages the descriptiveness of features extracted by convolutional neuralnetworks to estimate their density using normalizing flows. Normalizing flowsare well-suited to deal with low dimensional data distributions. However, theystruggle with the high dimensionality of images. Therefore, we employ amulti-scale feature extractor which enables the normalizing flow to assignmeaningful likelihoods to the images. Based on these likelihoods we develop ascoring function that indicates defects. Moreover, propagating the score backto the image enables pixel-wise localization. To achieve a high robustness andperformance we exploit multiple transformations in training and evaluation. Incontrast to most other methods, ours does not require a large number oftraining samples and performs well with as low as 16 images. We demonstrate thesuperior performance over existing approaches on the challenging and newlyproposed MVTec AD and Magnetic Tile Defects datasets.

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