Recently, in the field of artificial intelligence, multimodal learning has received a lot of attention due to expectations for the enhancement of AI performance and potential applications. Text-to-image generation, which is one of the multimodal tasks, is a challenging topic in computer vision and natural language processing. The text-to-image generation model based on generative adversarial network (GAN) utilizes a text encoder pre-trained with image-text pairs. However, text encoders pre-trained with image-text pairs cannot obtain rich information about texts not seen during pre-training, thus it is hard to generate an image that semantically matches a given text description. In this paper, we propose a new text-to-image generation model using pre-trained BERT, which is widely used in the field of natural language processing. The pre-trained BERT is used as a text encoder by performing fine-tuning with a large amount of text, so that rich information about the text is obtained and thus suitable for the image generation task. Through experiments using a multimodal benchmark dataset, we show that the proposed method improves the performance over the baseline model both quantitatively and qualitatively.

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