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Found 9 papers, 6 papers with code
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions
In the presence of imbalanced multi-class training data, GANs tend to favor classes with more samples, leading to the generation of low-quality and less diverse samples in tail classes.
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes
We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes.
StyleGenes: Discrete and Efficient Latent Distributions for GANs
Thus, by independently sampling a variant for each gene and combining them into the final latent vector, our approach can represent a vast number of unique latent samples from a compact set of learnable parameters.
NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions
Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors.
DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models
Scene extrapolation -- the idea of generating novel views by flying into a given image -- is a promising, yet challenging task.
Ranked #1 on
Perpetual View Generation
on LHQ
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Within the proposed benchmark, we explore some commonly known essentials of standard continual learning.
Arbitrary-Scale Image Synthesis
Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales.
Collapse by Conditioning: Training Class-conditional GANs with Limited Data
On the contrary, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability.
Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes.
