Search Results for author:
Found 11 papers, 6 papers with code
HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach
Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on.
Star-Shaped Denoising Diffusion Probabilistic Models
In the case of Gaussian distributions, SS-DDPM is equivalent to DDPM.
StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation
As a result of this study, we propose new efficient and lightweight parameterizations of StyleGAN for domain adaptation.
Iterative autoregression: a novel trick to improve your low-latency speech enhancement model
Streaming models are an essential component of real-time speech enhancement tools.
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space.
On Scaled Methods for Saddle Point Problems
Methods with adaptive scaling of different features play a key role in solving saddle point problems, primarily due to Adam's popularity for solving adversarial machine learning problems, including GANS training.
FFC-SE: Fast Fourier Convolution for Speech Enhancement
The FFC operator allows employing large receptive field operations within early layers of the neural network.
HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement
Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models.
Implicit λ-Jeffreys Autoencoders: Taking the Best of Both Worlds
We show that it takes the best properties of VAE and GAN objectives.
User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism.
Pairwise Augmented GANs with Adversarial Reconstruction Loss
We experimentally demonstrate that our model generates samples and reconstructions of quality competitive with state-of-the-art on datasets MNIST, CIFAR10, CelebA and achieves good quantitative results on CIFAR10.
