70 citations · 72 across the 7 of their papers we have counts for
7 papers
Smoothness Similarity Regularization for Few-Shot GAN Adaptation
Vadim Sushko, Ruyu Wang, Juergen Gall
The task of few-shot GAN adaptation aims to adapt a pre-trained GAN model to a small dataset with very few training images. While existing methods perform well when the dataset for…
Discovering Class-Specific GAN Controls for Semantic Image Synthesis
Edgar Schönfeld, Julio Borges, Vadim Sushko +2
Prior work has extensively studied the latent space structure of GANs for unconditional image synthesis, enabling global editing of generated images by the unsupervised discovery o…
One-Shot Synthesis of Images and Segmentation Masks
Vadim Sushko, Dan Zhang, Juergen Gall +1
Joint synthesis of images and segmentation masks with generative adversarial networks (GANs) is promising to reduce the effort needed for collecting image data with pixel-wise anno…
Decentralized Personalized Federated Learning for Min-Max Problems
Ekaterina Borodich, Aleksandr Beznosikov, Abdurakhmon Sadiev +4
Personalized Federated Learning (PFL) has witnessed remarkable advancements, enabling the development of innovative machine learning applications that preserve the privacy of train…
Learning to Generate Novel Scene Compositions from Single Images and Videos
Vadim Sushko, Juergen Gall, Anna Khoreva
Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to…
Generating Novel Scene Compositions from Single Images and Videos
Vadim Sushko, Dan Zhang, Juergen Gall +1
Given a large dataset for training, generative adversarial networks (GANs) can achieve remarkable performance for the image synthesis task. However, training GANs in extremely low…
You Only Need Adversarial Supervision for Semantic Image Synthesis
Vadim Sushko, Edgar Schönfeld, Dan Zhang +3
Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additiona…