Search Results for author: Maciej Zamorski

Found 7 papers, 4 papers with code

Continual learning on 3D point clouds with random compressed rehearsal

no code implementations16 May 2022 Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski, Tomasz Trzciński, Maciej Zięba

By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.

Continual Learning Visual Reasoning

Representing Point Clouds with Generative Conditional Invertible Flow Networks

1 code implementation7 Oct 2020 Michał Stypułkowski, Kacper Kania, Maciej Zamorski, Maciej Zięba, Tomasz Trzciński, Jan Chorowski

To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector.

Point Cloud Registration

Hypernetwork approach to generating point clouds

2 code implementations ICML 2020 Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zięba, Tomasz Trzciński

The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape.

Generating 3D Point Clouds

Conditional Invertible Flow for Point Cloud Generation

2 code implementations16 Oct 2019 Michał Stypułkowski, Maciej Zamorski, Maciej Zięba, Jan Chorowski

This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.

Point Cloud Generation

Generative Adversarial Networks: recent developments

no code implementations16 Mar 2019 Maciej Zamorski, Adrian Zdobylak, Maciej Zięba, Jerzy Świątek

In traditional generative modeling, good data representation is very often a base for a good machine learning model.

BIG-bench Machine Learning

Semi-supervised learning with Bidirectional GANs

no code implementations28 Nov 2018 Maciej Zamorski, Maciej Zięba

In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner.

General Classification Image Retrieval +1

Adversarial Autoencoders for Compact Representations of 3D Point Clouds

4 code implementations19 Nov 2018 Maciej Zamorski, Maciej Zięba, Piotr Klukowski, Rafał Nowak, Karol Kurach, Wojciech Stokowiec, Tomasz Trzciński

Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.

3D Object Retrieval Clustering +4

Cannot find the paper you are looking for? You can Submit a new open access paper.