Generalized Zero-Shot Learning Using Conditional Wasserstein Autoencoder
Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes. Conventionally, conditional generative models have been employed to generate training data for unseen classes from the attribute. In this paper, we propose a new conditional generative mod...
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| Vydáno v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 3413 - 3417 |
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IEEE
23.05.2022
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| ISSN: | 2379-190X |
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| Abstract | Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes. Conventionally, conditional generative models have been employed to generate training data for unseen classes from the attribute. In this paper, we propose a new conditional generative model that improves the GZSL performance greatly. In a nutshell, the proposed model, called conditional Wasserstein autoencoder (CWAE), minimizes the Wasserstein distance between the real and generated image feature distributions using an encoder-decoder architecture. From the extensive experiments on various benchmark datasets, we show that the proposed CWAE outperforms conventional generative models in terms of the GZSL classification performance. |
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| AbstractList | Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes. Conventionally, conditional generative models have been employed to generate training data for unseen classes from the attribute. In this paper, we propose a new conditional generative model that improves the GZSL performance greatly. In a nutshell, the proposed model, called conditional Wasserstein autoencoder (CWAE), minimizes the Wasserstein distance between the real and generated image feature distributions using an encoder-decoder architecture. From the extensive experiments on various benchmark datasets, we show that the proposed CWAE outperforms conventional generative models in terms of the GZSL classification performance. |
| Author | Kim, Junhan Shim, Byonghyo |
| Author_xml | – sequence: 1 givenname: Junhan surname: Kim fullname: Kim, Junhan email: junhankim@islab.snu.ac.kr organization: Seoul National University,Department of Electrical and Computer Engineering,Seoul,Korea – sequence: 2 givenname: Byonghyo surname: Shim fullname: Shim, Byonghyo email: bshim@islab.snu.ac.kr organization: Seoul National University,Department of Electrical and Computer Engineering,Seoul,Korea |
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| Snippet | Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes. Conventionally, conditional generative models... |
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| SubjectTerms | Acoustics Benchmark testing Conferences Data models Deep learning Generalized zero-shot learning generative adversarial network generative model Signal processing Training data variational autoencoder |
| Title | Generalized Zero-Shot Learning Using Conditional Wasserstein Autoencoder |
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