StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Abstract

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

Generative adversarial networks for anonymous acneic face dataset generation

“…1473 acneic face images) as input data. Transfer learning is widely used in deep learning because it allows training models from small datasets, decreasing the training time and the processing power [ 25 ].…”

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

“…The reliance on a large number of target domain data for constructing target learners can thus be reduced. It has emerged as a popular and promising area of machine learning due to its wide range of application possibilities, especially in solving real-world problems [ 78 , 79 , 80 , 81 ] in a cheaper and more reliable method. The sphere of use of transfer learning is not few, coupled with its record of high results [ 82 , 83 ].…”