State‐of‐the‐Art in the Architecture, Methods and Applications of StyleGAN

Abstract

Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. This state-of-the-art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. Among StyleGAN's most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well-behaved and remarkably disentangled. Combined with StyleGAN's visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator's learned distribution, and can only be applied to images generated by StyleGAN itself. Seeking to bring StyleGAN's latent control to real-world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. We map out StyleGAN's impressive story through these investigations, and discuss the details that have made StyleGAN the go-to generator. We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks. Looking forward, we point out StyleGAN's limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning.

GILEA: GAN Inversion-enabled latent eigenvalue analysis for phenome profiling and editing

“…Motivated by the impressive achievements of GAN inversion 40 , we instantiate LEA with the state-of-the-art GAN inversion model (Fig. 1(b)).…”

IST-editing: Infinite spatial transcriptomic editing in a generated gigapixel mouse pup

“…In natural image generation, previous generative models 12,13 typically utilize (spatial) noise input for unconditional image generation. In addition, learned textural representations 21 can be incorporated into the model to guide the image alterations 2 . However, semantic ambiguity often occurs in interpreting a single latent code and qualitative analysis is mostly made possible for a subset of representations 22 .…”

“…Recent advances in multi-omics technologies ( e.g ., spatial transcriptomics (ST) 1 ) and generative artificial intelligence (AI) 2,3 have the potential to revolutionize bioimage analysis 4 . Leveraging spatial co-profiling of high-plex mRNA transcripts (acting as proxies for gene expression) and high-resolution biomedical images, researchers possess unprecedented opportunities to model the complex spatial organization of living organisms.…”

“…Concurrently, generative AI 2,3 has showcased remarkable progress in creating high-quality visual content, paving the way towards novel applications in the biomedical domain. Trained with Hematoxylin and Eosin (H&E)-stained or (immuno)fluorescence images, prior studies 5–7 have achieved impressive results of bioimage generation and manipulation using GAN approaches.…”

StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing

“…proaches [6,10,14,18,24,38,39,42] which focus on image manipulation based on pretrained GANs. Given a target semantic attribute, they aim to manipulate the output image of a pretrained GAN.…”