DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis

Synthesizing high-quality realistic images from text descriptions is a challenging task. Almost all existing text-to-image Generative Adversarial Networks employ stacked architecture as the backbone. They utilize cross-modal attention mechanisms to fuse text and image features, and introduce extra networks to ensure text-image semantic consistency. In this work, we propose a much simpler, but more effective text-to-image model than previous works. Corresponding to the above three limitations, we propose: 1) a novel one-stage text-to-image backbone which is able to synthesize high-quality images directly by one pair of generator and discriminator, 2) a novel fusion module called deep text-image fusion block which deepens the text-image fusion process in generator, 3) a novel target-aware discriminator composed of matching-aware gradient penalty and one-way output which promotes the generator to synthesize more realistic and text-image semantic consistent images without introducing extra networks. Compared with existing text-to-image models, our proposed method (i.e., DF-GAN) is simpler but more efficient to synthesize realistic and text-matching images and achieves better performance. Extensive experiments on both Caltech-UCSD Birds 200 and COCO datasets demonstrate the superiority of the proposed model in comparison to state-of-the-art models

Synthetic Data Generation Using Wasserstein Conditional Gans With Gradient Penalty (WCGANS-GP)

With data protection requirements becoming stricter, the data privacy has become increasingly important and more crucial than ever. This has led to restrictions on the availability and dissemination of real-world datasets. Synthetic data offers a viable solution to overcome barriers of data access and sharing. Existing data generation methods require a great deal of user-defined rules, manual interactions and domainspecific knowledge. Moreover, they are not able to balance the trade-off between datausability and privacy. Deep learning based methods like GANs have seen remarkable success in synthesizing images by automatically learning the complicated distributions and patterns of real data. But they often suffer from instability during the training process