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2017 CVPR CVPR 2017

iCaRL: Incremental Classifier and Representation Learning

Abstract

A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
📈 Trend Setter — Continual Learning
🧭 Keyword Pioneer — class incremental
🐣 Hot Topic Early Bird — continual learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Sylvestre-Alvise Rebuffi , Alexander Kolesnikov , Georg Sperl , Christoph H. Lampert

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Learning Types > Continual Learning Machine Learning > Learning Paradigms > Continual Learning Deep Learning > Learning Types > Representation Learning Deep Learning > Learning Types > Continual Learning

Keywords

representation learning image classification continual learning incremental learning class incremental nearest exemplar
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