604 citations · 2.5k across the 63 of their papers we have counts for
9 papers · 1 filter
Differentiable Rendering with Reparameterized Volume Sampling
Nikita Morozov, Denis Rakitin, Oleg Desheulin +2
In view synthesis, a neural radiance field approximates underlying density and radiance fields based on a sparse set of scene pictures. To generate a pixel of a novel view, it marc…
StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation
Aibek Alanov, Vadim Titov, Maksim Nakhodnov +1
Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g. StyleGAN) to a specific domain with few samples (e.g. painting faces, sketches,…
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Aibek Alanov, Vadim Titov, Dmitry Vetrov
Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training dat…
User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks
Aibek Alanov, Max Kochurov, Denis Volkhonskiy +3
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly s…
Spatially Adaptive Computation Time for Residual Networks
Michael Figurnov, Maxwell D. Collins, Yukun Zhu +4
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is…
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
Michael Figurnov, Aijan Ibraimova, Dmitry Vetrov +1
We propose a novel approach to reduce the computational cost of evaluation of convolutional neural networks, a factor that has hindered their deployment in low-power devices such a…
Submodular relaxation for inference in Markov random fields
Anton Osokin, Dmitry Vetrov
In this paper we address the problem of finding the most probable state of a discrete Markov random field (MRF), also known as the MRF energy minimization problem. The task is know…
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Roman Shapovalov, Dmitry Vetrov, Anton Osokin +1
Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training. In this paper w…
Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints
Anton Osokin, Dmitry Vetrov, Vladimir Kolmogorov
In the paper we address the problem of finding the most probable state of discrete Markov random field (MRF) with associative pairwise terms. Although of practical importance, this…