Przemysław Spurek

research

∙ 09/21/2023

Face Identity-Aware Disentanglement in StyleGAN

Conditional GANs are frequently used for manipulating the attributes of ...

0 Adrian Suwała, et al. ∙

research

∙ 05/17/2023

MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation

NeRF is a popular model that efficiently represents 3D objects from 2D i...

0 Dominik Zimny, et al. ∙

research

∙ 04/03/2023

Gaussian model for closed curves

Gaussian Mixture Models (GMM) do not adapt well to curved and strongly n...

0 Krzysztof Byrski, et al. ∙

research

∙ 03/10/2023

NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering

Traditional 3D face models are based on mesh representations with textur...

0 Wojciech Zając, et al. ∙

research

∙ 02/09/2023

Hypernetworks build Implicit Neural Representations of Sounds

Implicit Neural Representations (INRs) are nowadays used to represent mu...

0 Filip Szatkowski, et al. ∙

research

∙ 01/27/2023

HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN

Recently, generative models for 3D objects are gaining much popularity i...

0 Adam Kania, et al. ∙

research

∙ 11/03/2022

HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks

Implicit neural representations (INRs) are a rapidly growing research fi...

0 Filip Szatkowski, et al. ∙

research

∙ 10/06/2022

Hypernetwork approach to Bayesian MAML

The main goal of Few-Shot learning algorithms is to enable learning from...

12 Piotr Borycki, et al. ∙

research

∙ 06/29/2022

LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood

Most of the existing methods for estimating the local intrinsic dimensio...

4 Piotr Tempczyk, et al. ∙

research

∙ 06/19/2022

Bounding Evidence and Estimating Log-Likelihood in VAE

Many crucial problems in deep learning and statistics are caused by a va...

0 Łukasz Struski, et al. ∙

research

∙ 06/16/2022

Continual Learning with Guarantees via Weight Interval Constraints

We introduce a new training paradigm that enforces interval constraints ...

0 Maciej Wołczyk, et al. ∙

research

∙ 03/21/2022

HyperShot: Few-Shot Learning by Kernel HyperNetworks

Few-shot models aim at making predictions using a minimal number of labe...

0 Marcin Sendera, et al. ∙

research

∙ 11/15/2021

Target Layer Regularization for Continual Learning Using Cramer-Wold Generator

We propose an effective regularization strategy (CW-TaLaR) for solving c...

0 Marcin Mazur, et al. ∙

research

∙ 10/26/2021

Non-Gaussian Gaussian Processes for Few-Shot Regression

Gaussian Processes (GPs) have been widely used in machine learning to mo...

11 Marcin Sendera, et al. ∙

research

∙ 10/12/2021

HyperCube: Implicit Field Representations of Voxelized 3D Models

Recently introduced implicit field representations offer an effective wa...

0 Magdalena Proszewska, et al. ∙

research

∙ 10/05/2021

Efficient GPU implementation of randomized SVD and its applications

Matrix decompositions are ubiquitous in machine learning, including appl...

0 Łukasz Struski, et al. ∙

research

∙ 08/10/2021

Flow-based SVDD for anomaly detection

We propose FlowSVDD – a flow-based one-class classifier for anomaly/outl...

0 Marcin Sendera, et al. ∙

research

∙ 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...

7 Ivan Kostiuk, et al. ∙

research

∙ 02/11/2021

Modeling 3D Surface Manifolds with a Locally Conditioned Atlas

Recently proposed 3D object reconstruction methods represent a mesh with...

12 Przemysław Spurek, et al. ∙

research

∙ 02/11/2021

HyperPocket: Generative Point Cloud Completion

Scanning real-life scenes with modern registration devices typically giv...

4 Przemysław Spurek, et al. ∙

research

∙ 11/30/2020

RegFlow: Probabilistic Flow-based Regression for Future Prediction

Predicting future states or actions of a given system remains a fundamen...

0 Maciej Zieba, et al. ∙

research

∙ 10/26/2020

Processing of incomplete images by (graph) convolutional neural networks

We investigate the problem of training neural networks from incomplete i...

0 Tomasz Danel, et al. ∙

research

∙ 10/06/2020

Flow-based anomaly detection

We propose OneFlow - a flow-based one-class classifier for anomaly (outl...

0 Łukasz Maziarka, et al. ∙

research

∙ 09/15/2020

Generative models with kernel distance in data space

Generative models dealing with modeling a joint data distribution are ge...

0 Szymon Knop, et al. ∙

research

∙ 06/17/2020

Adversarial Examples Detection and Analysis with Layer-wise Autoencoders

We present a mechanism for detecting adversarial examples based on data ...

0 Bartosz Wójcik, et al. ∙

research

∙ 06/15/2020

HyperFlow: Representing 3D Objects as Surfaces

In this work, we present HyperFlow - a novel generative model that lever...

0 Przemysław Spurek, et al. ∙

research

∙ 02/18/2020

LocoGAN – Locally Convolutional GAN

In the paper we construct a fully convolutional GAN model: LocoGAN, whic...

0 Łukasz Struski, et al. ∙

research

∙ 01/13/2020

WICA: nonlinear weighted ICA

Independent Component Analysis (ICA) aims to find a coordinate system in...

0 Andrzej Bedychaj, et al. ∙

research

∙ 09/11/2019

Geometric Graph Convolutional Neural Networks

Graph Convolutional Networks (GCNs) have recently become the primary cho...

1 Przemysław Spurek, et al. ∙

research

∙ 06/03/2019

Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models

We present an efficient technique, which allows to train classification ...

0 Paweł Morawiecki, et al. ∙

research

∙ 05/31/2019

Independent Component Analysis based on multiple data-weighting

Independent Component Analysis (ICA) - one of the basic tools in data an...

0 Andrzej Bedychaj, et al. ∙

research

∙ 05/30/2019

One-element Batch Training by Moving Window

Several deep models, esp. the generative, compare the samples from two d...

0 Przemysław Spurek, et al. ∙

research

∙ 03/01/2019

Non-linear ICA based on Cramer-Wold metric

Non-linear source separation is a challenging open problem with many app...

0 Przemysław Spurek, et al. ∙

research

∙ 01/29/2019

Sliced generative models

In this paper we discuss a class of AutoEncoder based generative models ...

0 Szymon Knop, et al. ∙

research

∙ 10/03/2018

Deep processing of structured data

We construct a general unified framework for learning representation of ...

0 Łukasz Maziarka, et al. ∙

research

∙ 05/23/2018

Cramer-Wold AutoEncoder

We propose a new generative model, Cramer-Wold Autoencoder (CWAE). Follo...

0 Jacek Tabor, et al. ∙

research

∙ 05/18/2018

Processing of missing data by neural networks

We propose a general, theoretically justified mechanism for processing m...

0 Marek Śmieja, et al. ∙

research

∙ 08/19/2015

Introduction to Cross-Entropy Clustering The R Package CEC

The R Package CEC performs clustering based on the cross-entropy cluster...

0 Jacek Tabor, et al. ∙

Przemysław Spurek

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