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cs.LG2023
Adapt & Align: Continual Learning with Generative Models Latent Space Alignment
Kamil Deja, Bartosz Cywiński, Jan Rybarczyk +1
In this work, we introduce Adapt & Align, a method for continual learning of neural networks by aligning latent representations in generative models. Neural Networks suffer from ab…
cs.LG2023
Bayesian Flow Networks in Continual Learning
Mateusz Pyla, Kamil Deja, Bartłomiej Twardowski +1
Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. The…