Abstract
We propose a novel approach for class incremental online learning in a limited data setting. This problem setting is challenging because of the following constraints: (1) Classes are given incrementally, which necessitates a class incremental learning approach; (2) Data for each class is given in an online fashion, i.e., each training example is seen only once during training; (3) Each class has very few training examples; and (4) We do not use or assume access to any replay/memory to store data from previous classes. Therefore, in this setting, we have to handle twofold problems of catastrophic forgetting and overfitting. In our approach, we learn robust representations that are generalizable across tasks without suffering from the problems of catastrophic forgetting and overfitting to accommodate future classes with limited samples. Our proposed method leverages the meta-learning framework with knowledge consolidation. The meta-learning framework helps the model for rapid learning when samples appear in an online fashion. Simultaneously, knowledge consolidation helps to learn a robust representation against forgetting under online updates to facilitate future learning. Our approach significantly outperforms other methods on several benchmarks.
Original language | English |
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Title of host publication | Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 |
Editors | Zhi-Hua Zhou |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 2621-2627 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241196 |
DOIs | |
Publication status | Published - 31 Aug 2021 |
Event | 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - Virtual, Online, Canada Duration: 19 Aug 2021 → 27 Aug 2021 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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ISSN (Print) | 1045-0823 |
Conference
Conference | 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 |
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Country/Territory | Canada |
City | Virtual, Online |
Period | 19/08/21 → 27/08/21 |
Funding
PR thanks support from Qualcomm Innovation Fellowship and Visvesvaraya Young Faculty Fellowship.
ASJC Scopus subject areas
- Artificial Intelligence