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Generative adversarial network (GAN) has achieved impressive success oncross-domain generation, but it faces difficulty in cross-modal generation dueto the lack of a common distribution between heterogeneous data. Most existingmethods of conditional based cross-modal GANs adopt the strategy ofone-directional transfer and have achieved preliminary success on text-to-imagetransfer. Instead of learning the transfer between different modalities, we aimto learn a synchronous latent space representing the cross-modal commonconcept. A novel network component named synchronizer is proposed in this workto judge whether the paired data is synchronous/corresponding or not, which canconstrain the latent space of generators in the GANs. Our GAN model, named asSyncGAN, can successfully generate synchronous data (e.g., a pair of image andsound) from identical random noise. For transforming data from one modality toanother, we recover the latent code by inverting the mappings of a generatorand use it to generate data of different modality. In addition, the proposedmodel can achieve semi-supervised learning, which makes our model more flexiblefor practical applications.

SyncGAN: Synchronize the Latent Space of Cross-modal Generative Adversarial Networks