iTAML: an incremental task-agnostic meta-learning approach

Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Mubarak Shah

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1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 33th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2020
EditorsCe Liu, Greg Mori, Kate Saenko, Silvio Savarese
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages13585-13594
Number of pages10
ISBN (Electronic)9781728171685
ISBN (Print)9781728171692
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2020 - Virtual, China
Duration: 14 Jun 202019 Jun 2020
http://cvpr2020.thecvf.com (Website )
https://openaccess.thecvf.com/CVPR2020 (Proceedings)
https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2020
Abbreviated titleCVPR 2020
CountryChina
CityVirtual
Period14/06/2019/06/20
Internet address

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