Semantic Feature Extraction for Generalized Zero-shot Learning
- URL: http://arxiv.org/abs/2112.14478v1
- Date: Wed, 29 Dec 2021 09:52:30 GMT
- Title: Semantic Feature Extraction for Generalized Zero-shot Learning
- Authors: Junhan Kim, Kyuhong Shim, and Byonghyo Shim
- Abstract summary: 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.
- Score: 23.53412767106488
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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