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Coupled adversarial variational autoencoder
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DOI：10.1016/j.image.2021.116396.png)
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Abstract
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Generating image is a hot research topic in the field of deep learning, and it is challenging for generating high quality image pairs. The image pair refers to the corresponding image tuples with the same high-level features and different low-level features, generating high-quality image pairs has important applications in some specific fields. Currently, there are many methods to generate high quality images, but these methods cannot produce higher resolution image pairs. To address this problem, we proposed a novel model which consists of two adversarial variational autoencoders, each one aim at generating an image of pairs more accurately. We called this model CoAdVAE (coupled adversarial variational autoencoders), it can generate high quality image pairs due to introducing adversarial learning to the model. In the experiments, we applied the proposed model to three learning tasks, i.e., generating image pairs with different attributes, converting image attributes, and image dehazing. We show by experiments compared with related approaches on four datasets, Mnist, Celeba, AFHQ, and Fog_data that the proposed model can achieve the-state-of-the-art results.
Keywords:
Image pairs
Adversarial variational autoencoder
Resolution
Adversarial learning
Attribute transformation
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