Affinity-Weighted RandAugment for Problem-Oriented Augmentation-Reference-Cited by-同舟云学术

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Affinity-Weighted RandAugment for Problem-Oriented Augmentation

Published:2024-06-30 Issue: Volume:32 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Park Yuna¹,Takase Tomoumi²,Kameyama Keisuke³,Onishi Masaki²

Affiliation:

1. University of Tsukuba,Degree Programs in Systems and Information Engineering and Graduate School of Science and Technology,Tsukuba,Japan,305-8577

2. National Institute of Advanced Industrial Science and Technology (AIST),Artificial Intelligence Research Center,Japan

3. University of Tsukuba,Institute of Systems and Information Engineering

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10651376.pdf?arnumber=10651376

Reference21 articles.

1. Population based augmentation: Efficient learning of augmentation policy schedules;Ho

2. Fast autoaugment;Lim;Advances in Neural Information Processing Systems,2019

3. Learning Data Augmentation Strategies for Object Detection

4. Autoaugment: Learning augmentation policies from data;Cubuk,2018

5. Randaugment: Practical automated data augmentation with a reduced search space

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