The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Surat ini mencadangkan model pengekod auto yang diselia oleh persamaan semantik untuk pembelajaran sifar pukulan. Dengan bantuan vektor kesamaan semantik bagi kelas yang dilihat dan yang tidak kelihatan serta cabang pengelasan, hasil eksperimen kami pada dua set data adalah 7.3% dan 4% lebih baik daripada pembelajaran tercanggih pada pembelajaran sifar pukulan konvensional dari segi purata. ketepatan 1 teratas.
Fengli SHEN
Zhejiang University
Zhe-Ming LU
Zhejiang University
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Salinan
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
Salinan
@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},}
Salinan
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 -