# ProGAN-Based Synthetic Image Generation for Industrial Visual Inspection of Metallic Parts

arXiv: https://arxiv.org/abs/2610.16287 · HTML: https://mlsanity.com/paper/2610.16287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 3 of 3)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Generative AI in Industrial Machine Vision -- A Review](https://mlsanity.com/paper/2408.10775/) (2024)
- [Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation](https://mlsanity.com/paper/1804.10323/) (2018)

## Abstract (arXiv)

Automated visual inspection is essential for efficient repair and recycling of metallic components. AI-based defect detection is a strong fit. However, it is often constrained by scarce, low-diversity training data. To address this gap, we study Generative AI-driven synthetic image generation using Progressive Growing GANs (ProGAN). We implement a ProGAN pipeline with Wasserstein loss and gradient penalty and evaluate key hyperparameters on two public datasets, MVTec AD (metal nut) and MPDD (tube). As a result, training remained stable and the model yielded realistic images. These images can augment training data, improve robustness, and reduce the cost of collecting rare defects.