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iTAML :
iTAML : An Incremental Task-Agnostic Meta-learning Approach
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- Rajasegaran, J. (author)
- Inception Institute of Artificial Intelligence, UAE
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- Khan, S. (author)
- Inception Institute of Artificial Intelligence, UAE
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- Hayat, M. (author)
- Inception Institute of Artificial Intelligence, UAE
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- Khan, Fahad Shahbaz, 1983- (author)
- Linköpings universitet,Datorseende,Tekniska fakulteten
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- Shah, M. (author)
- University of Central Florida, USA
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(creator_code:org_t)
- IEEE, 2020
- 2020
- English.
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In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). - : IEEE. - 9781728171685 ; , s. 13585-13594
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- Task analysis;Adaptation models;Training;Stability analysis;Interference;Predictive models;Heuristic algorithms
Publication and Content Type
- ref (subject category)
- kon (subject category)
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