  The two main impediments to continual learning 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. During training on a new
task, reconstructed images from encoded episodes are replayed in order 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
while using 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.
