Few-Shot Class Incremental Learning with Generative Feature Replay

Abhilash Reddy Shankarampeta; Koichiro Yamauchi

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Paper

Few-Shot Class Incremental Learning with Generative Feature Replay

Topics: Advanced Learning Methods; Deep Learning and Neural Networks; Image and Video Analysis and Understanding; Machine Learning Methods

In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 259-267, 2021

Authors: Abhilash Reddy Shankarampeta ¹ and Koichiro Yamauchi ²

Affiliations: ¹ Department of EEE, Indian Institute of Technology Guwahati, India ; ² Department of Informationn Science, Chubu University, Japan

Keyword(s): Few-Shot Learning, Incremental Learning, Catastrophic Forgetting, Generative Feature Replay.

Abstract: The humans can learn novel concepts from only a few examples effortlessly and learn additional tasks without forgetting previous ones. Making machines to learn incrementally from only a few instances is very challenging due to catastrophic forgetting between new and previously learned tasks; this can be solved by generative image replay. However, image generation with only a few examples is a challenging task. In this work, we propose a feature replay approach instead of image replay for few-shot learning scenarios. A feature extractor with feature distillation is combined with feature replay at the classifier level to tackle catastrophic forgetting.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Shankarampeta, A. and Yamauchi, K. (2021). Few-Shot Class Incremental Learning with Generative Feature Replay. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 259-267. DOI: 10.5220/0010246602590267

@conference{icpram21,
author={Abhilash Reddy Shankarampeta. and Koichiro Yamauchi.},
title={Few-Shot Class Incremental Learning with Generative Feature Replay},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={259-267},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010246602590267},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Few-Shot Class Incremental Learning with Generative Feature Replay
SN - 978-989-758-486-2
IS - 2184-4313
AU - Shankarampeta, A.
AU - Yamauchi, K.
PY - 2021
SP - 259
EP - 267
DO - 10.5220/0010246602590267
PB - SciTePress