Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation

Ruyu Wang (Robert Bosch GmbH),* Sabrina Hoppe (Bosch), Eduardo Monari (Robert Bosch Coorporate Research), Marco Huber (University of Stuttgart)
The 33rd British Machine Vision Conference


Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches. For example, on highly optimized production lines, defective samples are hardly acquired while non-defective samples come almost for free. The defects however often seem to resemble each other, e.g. scratches on different products may only differ in few characteristics. In this work, we introduce a framework, Defect Transfer GAN (DT-GAN), which learns to represent defect types independent of and across various background products and yet can apply defect-specific styles to generate realistic defective images. An empirical study on the MVTec AD and two additional datasets showcase DT-GAN outperforms state-of-the-art image synthesis methods w.r.t. sample fidelity and diversity in defect generation. We further demonstrate benefits for a critical downstream task in manufacturing---defect classification. Results show that the augmented data from DT-GAN provides consistent gains even in the few samples regime and reduces the error rate up to 51% compared to both traditional and advanced data augmentation methods.



author    = {Ruyu Wang and Sabrina Hoppe and Eduardo Monari and Marco Huber},
title     = {Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {}

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