  Generalized zero-shot learning (GZSL) is a technique to train a deep learning
model to identify unseen classes using the attribute. In this paper, we put
forth a new GZSL technique that improves the GZSL classification performance
greatly. Key idea of the proposed approach, henceforth referred to as semantic
feature extraction-based GZSL (SE-GZSL), is to use the semantic feature
containing only attribute-related information in learning the relationship
between the image and the attribute. In doing so, we can remove the
interference, if any, caused by the attribute-irrelevant information contained
in the image feature. To train a network extracting the semantic feature, we
present two novel loss functions, 1) mutual information-based loss to capture
all the attribute-related information in the image feature and 2)
similarity-based loss to remove unwanted attribute-irrelevant information. From
extensive experiments using various datasets, we show that the proposed SE-GZSL
technique outperforms conventional GZSL approaches by a large margin.
