Generate the corresponding Image from Text Description using Modified GAN-CLS Algorithm
This is an incremental improvement for text-to-image synthesis, addressing specific issues in a known method.
The paper tackled the problem of generating images from text descriptions by modifying the GAN-CLS algorithm to correct issues identified through inference, resulting in more plausible images and better text matching in some cases on the Oxford-102 and CUB datasets.
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.