  Synthesizing images or texts automatically is a useful research area in the
artificial intelligence nowadays. Generative adversarial networks (GANs), which
are proposed by Goodfellow in 2014, make this task to be done more efficiently
by using deep neural networks. We consider generating corresponding images from
an input text description using a GAN. In this paper, we analyze the GAN-CLS
algorithm, which is a kind of advanced method of GAN proposed by Scott Reed in
2016. First, we find the problem with this algorithm through inference. Then we
correct the GAN-CLS algorithm according to the inference by modifying the
objective function of the model. Finally, we do the experiments on the
Oxford-102 dataset and the CUB dataset. As a result, our modified algorithm can
generate images which are more plausible than the GAN-CLS algorithm in some
cases. Also, some of the generated images match the input texts better.
