  Multiple modalities for certain information provide a variety of perspectives
on that information, which can improve the understanding of the information.
Thus, it may be crucial to generate data of different modality from the
existing data to enhance the understanding. In this paper, we investigate the
cross-modal audio-to-image generation problem and propose Cross-Modal
Contrastive Representation Learning (CMCRL) to extract useful features from
audios and use it in the generation phase. Experimental results show that CMCRL
enhances quality of images generated than previous research.
