  The two main challenges faced by continual learning approaches are
catastrophic forgetting and memory limitations on the storage of data. To cope
with these challenges, we propose a novel, cognitively-inspired approach which
trains autoencoders with Neural Style Transfer to encode and store images.
Reconstructed images from encoded episodes are replayed when training the
classifier model on a new task to avoid catastrophic forgetting. The loss
function for the reconstructed images is weighted to reduce its effect during
classifier training to cope with image degradation. When the system runs out of
memory the encoded episodes are converted into centroids and covariance
matrices, which are used to generate pseudo-images during classifier training,
keeping classifier performance stable with less memory. Our approach increases
classification accuracy by 13-17% over state-of-the-art methods on benchmark
datasets, while requiring 78% less storage space.
