  We hypothesize that disentangled feature representations suffer less from
catastrophic forgetting. As a case study we perform explicit disentanglement of
color and shape, by adjusting the network architecture. We tested
classification accuracy and forgetting in a task-incremental setting with
Oxford-102 Flowers dataset. We combine our method with Elastic Weight
Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory
Aware Synapses, and show that feature disentanglement positively impacts
continual learning performance.
