Search Results for author: Marcin Mazur

Found 11 papers, 7 papers with code

PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification

no code implementations17 Jun 2024 Magdalena Trędowicz, Łukasz Struski, Marcin Mazur, Szymon Janusz, Arkadiusz Lewicki, Jacek Tabor

Video processing is generally divided into two main categories: processing of the entire video, which typically yields optimal classification outcomes, and real-time processing, where the objective is to make a decision as promptly as possible.

Classification Video Classification

HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation

1 code implementation2 Feb 2024 Paweł Batorski, Dawid Malarz, Marcin Przewięźlikowski, Marcin Mazur, Sławomir Tadeja, Przemysław Spurek

Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images.

Few-Shot Learning Object

Bounding Evidence and Estimating Log-Likelihood in VAE

no code implementations19 Jun 2022 Łukasz Struski, Marcin Mazur, Paweł Batorski, Przemysław Spurek, Jacek Tabor

Many crucial problems in deep learning and statistics are caused by a variational gap, i. e., a difference between evidence and evidence lower bound (ELBO).

HyperCube: Implicit Field Representations of Voxelized 3D Models

1 code implementation12 Oct 2021 Magdalena Proszewska, Marcin Mazur, Tomasz Trzciński, Przemysław Spurek

Recently introduced implicit field representations offer an effective way of generating 3D object shapes.

Decoder

Generative models with kernel distance in data space

1 code implementation15 Sep 2020 Szymon Knop, Marcin Mazur, Przemysław Spurek, Jacek Tabor, Igor Podolak

First, an autoencoder based architecture, using kernel measures, is built to model a manifold of data.

Sliced generative models

no code implementations29 Jan 2019 Szymon Knop, Marcin Mazur, Jacek Tabor, Igor Podolak, Przemysław Spurek

In this paper we discuss a class of AutoEncoder based generative models based on one dimensional sliced approach.

Cramer-Wold AutoEncoder

2 code implementations ICLR 2019 Szymon Knop, Jacek Tabor, Przemysław Spurek, Igor Podolak, Marcin Mazur, Stanisław Jastrzębski

The crucial new ingredient is the introduction of a new (Cramer-Wold) metric in the space of densities, which replaces the Wasserstein metric used in SWAE.

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