Abstract
Generative Adversarial Networks (GANs) can learn various generative models such as probability distribution and images, while it is difficult to converge training. There are few successful methods for generating high-resolution images. In this paper, we propose the parallel-pathway generator network to generate high-resolution natural images. Our parallel network are constructed by parallelly stacked generators with different structure. To investigate the effect of our structure, we apply it to two image generation tasks: human-face image and road image which does not have square resolution. Results indicate that our method can generate high-resolution natural images with few parameter tuning.
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Okadome, Y., Wei, W., Aizono, T. (2017). Parallel-Pathway Generator for Generative Adversarial Networks to Generate High-Resolution Natural Images. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_74
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DOI: https://doi.org/10.1007/978-3-319-68612-7_74
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