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cs.LG2022
Logarithmic Continual Learning
Wojciech Masarczyk, Paweł Wawrzyński, Daniel Marczak +2
We introduce a neural network architecture that logarithmically reduces the number of self-rehearsal steps in the generative rehearsal of continually learned models. In continual l…
cs.LG2020
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…
cs.LG2020
End-to-end Sinkhorn Autoencoder with Noise Generator
Kamil Deja, Jan Dubiński, Piotr Nowak +2
In this work, we propose a novel end-to-end sinkhorn autoencoder with noise generator for efficient data collection simulation. Simulating processes that aim at collecting experime…