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In this work, we improve the generative replay in a continual learningsetting to perform well on challenging scenarios. Current generative rehearsalmethods are usually benchmarked on small and simple datasets as they are notpowerful 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 thefact that the generated features are far from the original ones when mapped tothe latent space. Therefore, we propose three modifications that allow themodel to learn and generate complex data. More specifically, we incorporate thedistillation in latent space between the current and previous models to reducefeature drift. Additionally, a latent matching for the reconstruction andoriginal data is proposed to improve generated features alignment. Further,based on the observation that the reconstructions are better for preservingknowledge, we add the cycling of generations through the previously trainedmodel to make them closer to the original data. Our method outperforms othergenerative replay methods in various scenarios. Code available athttps://github.com/valeriya-khan/looking-through-the-past.

Looking through the past: better knowledge retention for generative replay in continual learning