Looking through the past: better knowledge retention for generative
  replay in continual learning
Valeriya Khan Sebastian Cygert Kamil Deja Tomasz Trzciński Bartłomiej Twardowski
In this work, we improve the generative replay in a continual learning
setting to perform well on challenging scenarios. Current generative rehearsal
methods are usually benchmarked on small and simple datasets as they are not
powerful enough to generate more complex data with a greater number of classes.
We notice that in VAE-based generative replay, this could be attributed to the
fact that the generated features are far from the original ones when mapped to
the latent space. Therefore, we propose three modifications that allow the
model to learn and generate complex data. More specifically, we incorporate the
distillation in latent space between the current and previous models to reduce
feature drift. Additionally, a latent matching for the reconstruction and
original data is proposed to improve generated features alignment. Further,
based on the observation that the reconstructions are better for preserving
knowledge, we add the cycling of generations through the previously trained
model to make them closer to the original data. Our method outperforms other
generative replay methods in various scenarios. Code available at
https://github.com/valeriya-khan/looking-through-the-past.