Abstract | ||
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Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new task. In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks. In this pursuit, we introduce a novel meta-learning approach that seeks to maintain an equilibrium between all the encountered tasks. This is ensured by a new meta-update rule which avoids catastrophic forgetting. In comparison to previous meta-learning techniques, our approach is task-agnostic. When presented with a continuum of data, our model automatically identifies the task and quickly adapts to it with just a single update. We perform extensive experiments on five datasets in a class-incremental setting, leading to significant improvements over the state of the art methods (e.g., a 21.3% boost on CIFAR100 with 10 incremental tasks). Specifically, on large-scale datasets that generally prove difficult cases for incremental learning, our approach delivers absolute gains as high as 19.1% and 7.4% on ImageNet and MS-Celeb datasets, respectively. |
Year | DOI | Venue |
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2020 | 10.1109/CVPR42600.2020.01360 | CVPR |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
19 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jathushan Rajasegaran | 1 | 13 | 4.62 |
Salman Khan | 2 | 387 | 41.05 |
Munawar Hayat | 3 | 315 | 19.30 |
Fahad Shahbaz Khan | 4 | 1622 | 69.24 |
Mubarak Shah | 5 | 16522 | 943.74 |