Same Same but Different: Augmentation of Tiny Industrial Datasets using Generative Adversarial Networks

The evolution of generative adversarial networks has permitted the generation of realistic fake images which, in some cases, are indistinguishable from the real ones. Many recent works in image generation focus on learning internal image statistics via training only on a single natural image. While natural images exhibit a variability in their attributes, industrial images are often acquired in a controlled environment following a specific structure. In this work we utilize the cutting-edge results of single image generation on the structured case of industrial images. Deep Learning plays an important role in Industry 4.0 manufacturing lines and multiple ML-based image processing products are currently on the market. To be able to tackle a variety of problems where image acquisition is costly and time-consuming data generation is a promising approach. The proposed method only requires a handful of images for training, making it an ideal candidate for industrial application where data is scarce and confidential. It provides the foundation for a variety of use cases in the field of industrial Deep Learning.