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09/21/2023
Face Identity-Aware Disentanglement in StyleGAN
Conditional GANs are frequently used for manipulating the attributes of ...
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05/17/2023
MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation
NeRF is a popular model that efficiently represents 3D objects from 2D i...
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04/03/2023
Gaussian model for closed curves
Gaussian Mixture Models (GMM) do not adapt well to curved and strongly n...
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03/10/2023
NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering
Traditional 3D face models are based on mesh representations with textur...
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02/09/2023
Hypernetworks build Implicit Neural Representations of Sounds
Implicit Neural Representations (INRs) are nowadays used to represent mu...
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01/27/2023
HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN
Recently, generative models for 3D objects are gaining much popularity i...
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11/03/2022
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
Implicit neural representations (INRs) are a rapidly growing research fi...
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10/06/2022
Hypernetwork approach to Bayesian MAML
The main goal of Few-Shot learning algorithms is to enable learning from...
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06/29/2022
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Most of the existing methods for estimating the local intrinsic dimensio...
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06/19/2022
Bounding Evidence and Estimating Log-Likelihood in VAE
Many crucial problems in deep learning and statistics are caused by a va...
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06/16/2022
Continual Learning with Guarantees via Weight Interval Constraints
We introduce a new training paradigm that enforces interval constraints ...
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03/21/2022
HyperShot: Few-Shot Learning by Kernel HyperNetworks
Few-shot models aim at making predictions using a minimal number of labe...
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11/15/2021
Target Layer Regularization for Continual Learning Using Cramer-Wold Generator
We propose an effective regularization strategy (CW-TaLaR) for solving c...
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10/26/2021
Non-Gaussian Gaussian Processes for Few-Shot Regression
Gaussian Processes (GPs) have been widely used in machine learning to mo...
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10/12/2021
HyperCube: Implicit Field Representations of Voxelized 3D Models
Recently introduced implicit field representations offer an effective wa...
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10/05/2021
Efficient GPU implementation of randomized SVD and its applications
Matrix decompositions are ubiquitous in machine learning, including appl...
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08/10/2021
Flow-based SVDD for anomaly detection
We propose FlowSVDD – a flow-based one-class classifier for anomaly/outl...
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08/03/2021
HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population
Designing a 3D game scene is a tedious task that often requires a substa...
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02/11/2021
Modeling 3D Surface Manifolds with a Locally Conditioned Atlas
Recently proposed 3D object reconstruction methods represent a mesh with...
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02/11/2021
HyperPocket: Generative Point Cloud Completion
Scanning real-life scenes with modern registration devices typically giv...
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11/30/2020
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Predicting future states or actions of a given system remains a fundamen...
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10/26/2020
Processing of incomplete images by (graph) convolutional neural networks
We investigate the problem of training neural networks from incomplete i...
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10/06/2020
Flow-based anomaly detection
We propose OneFlow - a flow-based one-class classifier for anomaly (outl...
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09/15/2020
Generative models with kernel distance in data space
Generative models dealing with modeling a joint data distribution are ge...
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06/17/2020
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
We present a mechanism for detecting adversarial examples based on data ...
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06/15/2020
HyperFlow: Representing 3D Objects as Surfaces
In this work, we present HyperFlow - a novel generative model that lever...
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02/18/2020
LocoGAN – Locally Convolutional GAN
In the paper we construct a fully convolutional GAN model: LocoGAN, whic...
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01/13/2020
WICA: nonlinear weighted ICA
Independent Component Analysis (ICA) aims to find a coordinate system in...
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09/11/2019
Geometric Graph Convolutional Neural Networks
Graph Convolutional Networks (GCNs) have recently become the primary cho...
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06/03/2019
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
We present an efficient technique, which allows to train classification ...
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05/31/2019
Independent Component Analysis based on multiple data-weighting
Independent Component Analysis (ICA) - one of the basic tools in data an...
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05/30/2019
One-element Batch Training by Moving Window
Several deep models, esp. the generative, compare the samples from two d...
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03/01/2019
Non-linear ICA based on Cramer-Wold metric
Non-linear source separation is a challenging open problem with many app...
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01/29/2019
Sliced generative models
In this paper we discuss a class of AutoEncoder based generative models ...
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10/03/2018
Deep processing of structured data
We construct a general unified framework for learning representation of ...
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05/23/2018
Cramer-Wold AutoEncoder
We propose a new generative model, Cramer-Wold Autoencoder (CWAE). Follo...
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05/18/2018
Processing of missing data by neural networks
We propose a general, theoretically justified mechanism for processing m...
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08/19/2015
