Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation

• URL: http://arxiv.org/abs/2302.08366v1
• Date: Thu, 16 Feb 2023 15:35:21 GMT
• Title: Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation
• Authors: Ruyu Wang, Sabrina Hoppe, Eduardo Monari and Marco F. Huber
• Abstract summary: Defect Transfer GAN (DT-GAN) learns to represent defect types independent of and across various background products. An empirical study on the MVTec AD and two additional datasets showcase DT-GAN outperforms state-of-the-art image synthesis methods. Results show that the augmented data from DT-GAN provides consistent gains even in the few samples regime.
• Score: 4.559353193715442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 a 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.

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