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A Semantic Similarity Supervised Autoencoder for Zero-Shot Learning
Fengli SHEN Zhe-Ming LU
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2020/06/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
zero-shot learning, autoencoder, image classification,
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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.