4 citations · 5 across the 4 of their papers we have counts for
4 papers
Exploring Continual Learning of Diffusion Models
Michał Zając, Kamil Deja, Anna Kuzina +4
Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data. However, tra…
Learning Data Representations with Joint Diffusion Models
Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak
Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. In this work, we depart from…
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
Kamil Deja, Paweł Wawrzyński, Wojciech Masarczyk +2
We propose a new method for unsupervised generative continual learning through realignment of Variational Autoencoder's latent space. Deep generative models suffer from catastrophi…
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning
Kamil Deja, Paweł Wawrzyński, Daniel Marczak +2
We introduce a binary latent space autoencoder architecture to rehearse training samples for the continual learning of neural networks. The ability to extend the knowledge of a mod…