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Found 7 papers, 4 papers with code
Smoothness Similarity Regularization for Few-Shot GAN Adaptation
The task of few-shot GAN adaptation aims to adapt a pre-trained GAN model to a small dataset with very few training images.
Discovering Class-Specific GAN Controls for Semantic Image Synthesis
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 of interpretable latent directions.
One-Shot Synthesis of Images and Segmentation Masks
To this end, inspired by the recent architectural developments of single-image GANs, we introduce our OSMIS model which enables the synthesis of segmentation masks that are precisely aligned to the generated images in the one-shot regime.
Decentralized Personalized Federated Learning for Min-Max Problems
Personalized Federated Learning (PFL) has witnessed remarkable advancements, enabling the development of innovative machine learning applications that preserve the privacy of training data.
Learning to Generate Novel Scene Compositions from Single Images and Videos
Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence.
Generating Novel Scene Compositions from Single Images and Videos
In this work, we introduce SIV-GAN, an unconditional generative model that can generate new scene compositions from a single training image or a single video clip.
You Only Need Adversarial Supervision for Semantic Image Synthesis
By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity with better alignment to their input label maps, making the use of the perceptual loss superfluous.
