Adversarial Continual Learning

• URL: http://arxiv.org/abs/2003.09553v2
• Date: Tue, 21 Jul 2020 15:42:20 GMT
• Title: Adversarial Continual Learning
• Authors: Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, Marcus Rohrbach
• Abstract summary: We propose a hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills.
• Score: 99.56738010842301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demonstrate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at \url{https://github.com/facebookresearch/Adversarial-Continual-Learning}.

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