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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Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 3413 - 3417
Hauptverfasser: Kim, Junhan, Shim, Byonghyo
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 23.05.2022
Schlagworte:
Acoustics
Benchmark testing
Conferences
Data models
Deep learning
Generalized zero-shot learning
generative adversarial network
generative model
Signal processing
Training data
variational autoencoder
ISSN:2379-190X
Online-Zugang:Volltext
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Zusammenfassung: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.
ISSN:2379-190X
DOI:10.1109/ICASSP43922.2022.9747741