Disentanglement of Color and Shape Representations for Continual Learning

David Berga; Marc Masana; Joost Van de Weijer

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arXiv · 2007.06356

Disentanglement of Color and Shape Representations for Continual Learning

David Berga·Marc Masana·Joost Van de Weijer

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Abstract

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.

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David Berga, Marc Masana, Joost Van de Weijer. 2020-07-13. Disentanglement of Color and Shape Representations for Continual Learning. https://arxiv.org/abs/2007.06356

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