3 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
Continual learning on 3D point clouds with random compressed rehearsal
Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski +2
Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype…
Conditional Invertible Flow for Point Cloud Generation
Michał Stypułkowski, Maciej Zamorski, Maciej Zięba +1
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a pro…
Generative Adversarial Networks: recent developments
Maciej Zamorski, Adrian Zdobylak, Maciej Zięba +1
In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanat…
Semi-supervised learning with Bidirectional GANs
Maciej Zamorski, Maciej Zięba
In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner. The presented method utilizes triplet loss funct…
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Maciej Zamorski, Maciej Zięba, Piotr Klukowski +4
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtai…