A Semantic Similarity Supervised Autoencoder for Zero-Shot Learning
Summary :
This Letter proposes a autoencoder model supervised by semantic similarity for zero-shot learning. With the help of semantic similarity vectors of seen and unseen classes and the classification branch, our experimental results on two datasets are 7.3% and 4% better than the state-of-the-art on conventional zero-shot learning in terms of the averaged top-1 accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.6 pp.1419-1422
- Publication Date
- 2020/06/01
- Publicized
- 2020/03/03
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDL8176
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Fengli SHEN
Zhejiang University
Zhe-Ming LU
Zhejiang University
Keyword
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Fengli SHEN, Zhe-Ming LU, "A Semantic Similarity Supervised Autoencoder for Zero-Shot Learning" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 6, pp. 1419-1422, June 2020, doi: 10.1587/transinf.2019EDL8176.
Abstract: This Letter proposes a autoencoder model supervised by semantic similarity for zero-shot learning. With the help of semantic similarity vectors of seen and unseen classes and the classification branch, our experimental results on two datasets are 7.3% and 4% better than the state-of-the-art on conventional zero-shot learning in terms of the averaged top-1 accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8176/_p
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@ARTICLE{e103-d_6_1419,
author={Fengli SHEN, Zhe-Ming LU, },
journal={IEICE TRANSACTIONS on Information},
title={A Semantic Similarity Supervised Autoencoder for Zero-Shot Learning},
year={2020},
volume={E103-D},
number={6},
pages={1419-1422},
abstract={This Letter proposes a autoencoder model supervised by semantic similarity for zero-shot learning. With the help of semantic similarity vectors of seen and unseen classes and the classification branch, our experimental results on two datasets are 7.3% and 4% better than the state-of-the-art on conventional zero-shot learning in terms of the averaged top-1 accuracy.},
keywords={},
doi={10.1587/transinf.2019EDL8176},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - A Semantic Similarity Supervised Autoencoder for Zero-Shot Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1419
EP - 1422
AU - Fengli SHEN
AU - Zhe-Ming LU
PY - 2020
DO - 10.1587/transinf.2019EDL8176
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2020
AB - This Letter proposes a autoencoder model supervised by semantic similarity for zero-shot learning. With the help of semantic similarity vectors of seen and unseen classes and the classification branch, our experimental results on two datasets are 7.3% and 4% better than the state-of-the-art on conventional zero-shot learning in terms of the averaged top-1 accuracy.
ER -
English
