2020/03/21 by Sayna Ebrahimi, Franziska Meier, Ebrahimi, Sayna +7 · 12 citations
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2003.09553
Accepted at ECCV 2020
arxiv created 2020/07/21 · arxiv updated 2020/07/22
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 \urlhttps://github.com/facebookresearch/Adversarial-Continual-Learning.