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About this paper
Hans Aoyang Zhou, Dominik Wolfschläger, Constantinos Florides, Jonas Werheid, Hannes Behnen, Jan-Henrick Woltersmann, Tiago C. Pinto, Marco Kemmerling and 2 more
Researchers from RWTH Aachen University and collaborators conducted a systematic literature review to map the current state of Generative AI applications in industrial machine vision. The review identifies dominant GenAI architectures, key properties for successful industrial transfer, and the primary purposes—such as data augmentation and image enhancement—across various industrial vision tasks.
Key insights
- Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are the most frequently applied GenAI architectures in industrial machine vision, while diffusion and autoregressive models are largely absent due to high sampling times.
- Successful industrial application of GenAI models often favors simpler architectures with stable training, clear practical applicability, and strong theoretical backgrounds over complex state-of-the-art models.
- Data quality, diversity, and specific preprocessing requirements are crucial, with GenAI being effective in mitigating data scarcity (e.g., for rare defects) but still facing issues like mode collapse and training instability.
Summary by alphaXiv.
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