Learning to Continually Learn Rapidly from Few and Noisy Data

• URL: http://arxiv.org/abs/2103.04066v1
• Date: Sat, 6 Mar 2021 08:29:47 GMT
• Title: Learning to Continually Learn Rapidly from Few and Noisy Data
• Authors: Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, and Hanna Suominen
• Abstract summary: Continual learning could be achieved via replay -- by concurrently training externally stored old data while learning a new task. By employing a meta-learner, which textitlearns a learning rate per parameter per past task, we found that base learners produced strong results when less memory was available.
• Score: 19.09933805011466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution. Continual learning could be achieved via replay -- by concurrently training externally stored old data while learning a new task. However, replay becomes less effective when each past task is allocated with less memory. To overcome this difficulty, we supplemented replay mechanics with meta-learning for rapid knowledge acquisition. By employing a meta-learner, which \textit{learns a learning rate per parameter per past task}, we found that base learners produced strong results when less memory was available. Additionally, our approach inherited several meta-learning advantages for continual learning: it demonstrated strong robustness to continually learn under the presence of noises and yielded base learners to higher accuracy in less updates.

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