An Acceleration Framework for High Resolution Image Synthesis
Jinlin Liu, Yuan Yao, Jianqiang Ren

TL;DR
This paper introduces a two-stage acceleration framework for high resolution image synthesis with GANs, significantly reducing training time and hardware requirements by operating in a compressed latent space.
Contribution
The proposed method accelerates high resolution image synthesis training by transforming images into a smaller latent space and training a code generator, enabling high-quality results with limited hardware.
Findings
Training time reduced to 3 days for 1024x1024 images
High quality of generated images maintained or improved
Requires only one high-end GPU for large images
Abstract
Synthesis of high resolution images using Generative Adversarial Networks (GANs) is challenging, which usually requires numbers of high-end graphic cards with large memory and long time of training. In this paper, we propose a two-stage framework to accelerate the training process of synthesizing high resolution images. High resolution images are first transformed to small codes via the trained encoder and decoder networks. The code in latent space is times smaller than the original high resolution images. Then, we train a code generation network to learn the distribution of the latent codes. In this way, the generator only learns to generate small latent codes instead of large images. Finally, we decode the generated latent codes to image space via the decoder networks so as to output the synthesized high resolution images. Experimental results show that the proposed method accelerates…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques · Advanced Vision and Imaging
