1 code implementation • 1 Apr 2024 • Maxim Nikolaev, Mikhail Kuznetsov, Dmitry Vetrov, Aibek Alanov
Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on.
1 code implementation • NeurIPS 2023 • Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry Vetrov
In the case of Gaussian distributions, SS-DDPM is equivalent to DDPM.
1 code implementation • ICCV 2023 • Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry Vetrov
As a result of this study, we propose new efficient and lightweight parameterizations of StyleGAN for domain adaptation.
no code implementations • 3 Nov 2022 • Pavel Andreev, Nicholas Babaev, Azat Saginbaev, Ivan Shchekotov, Aibek Alanov
Streaming models are an essential component of real-time speech enhancement tools.
1 code implementation • 17 Oct 2022 • Aibek Alanov, Vadim Titov, Dmitry Vetrov
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.
no code implementations • 16 Jun 2022 • Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev, Martin Takáč, Alexander Gasnikov
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.
1 code implementation • 6 Apr 2022 • Ivan Shchekotov, Pavel Andreev, Oleg Ivanov, Aibek Alanov, Dmitry Vetrov
The FFC operator allows employing large receptive field operations within early layers of the neural network.
2 code implementations • 24 Mar 2022 • Pavel Andreev, Aibek Alanov, Oleg Ivanov, Dmitry Vetrov
Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models.
no code implementations • ICLR 2020 • Aibek Alanov, Max Kochurov, Artem Sobolev, Daniil Yashkov, Dmitry Vetrov
We show that it takes the best properties of VAE and GAN objectives.
no code implementations • 9 Apr 2019 • Aibek Alanov, Max Kochurov, Denis Volkhonskiy, Daniil Yashkov, Evgeny Burnaev, Dmitry Vetrov
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism.
no code implementations • ICLR 2019 • Aibek Alanov, Max Kochurov, Daniil Yashkov, Dmitry Vetrov
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.