187 citations · 234 across the 5 of their papers we have counts for
6 papers
When, Why, and Which Pretrained GANs Are Useful?
Timofey Grigoryev, Andrey Voynov, Artem Babenko
The literature has proposed several methods to finetune pretrained GANs on new datasets, which typically results in higher performance compared to training from scratch, especially…
Label-Efficient Semantic Segmentation with Diffusion Models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov +2
Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-t…
Navigating the GAN Parameter Space for Semantic Image Editing
Anton Cherepkov, Andrey Voynov, Artem Babenko
Generative Adversarial Networks (GANs) are currently an indispensable tool for visual editing, being a standard component of image-to-image translation and image restoration pipeli…
Object Segmentation Without Labels with Large-Scale Generative Models
Andrey Voynov, Stanislav Morozov, Artem Babenko
The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to down…
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Andrey Voynov, Artem Babenko
The latent spaces of GAN models often have semantically meaningful directions. Moving in these directions corresponds to human-interpretable image transformations, such as zooming…
RPGAN: GANs Interpretability via Random Routing
Andrey Voynov, Artem Babenko
In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) -- an alternative design of GANs that can serve as a tool for generative model analysis. While the la…