Text to Image GANs with RoBERTa and Fine-grained Attention Networks

Synthesizing new images from textual descriptions requires understanding the context of the text. It is a very challenging problem in Natural Language Processing and Computer vision. Existing systems use Generative Adversarial Network (GAN) to generate images using a simple text encoder from their captions. This paper consist synthesizing images from textual descriptions using Caltech-UCSD birds datasets by baselining the generative model using Attentional Generative Adversarial Networks (AttnGAN) and using RoBERTa pre-trained neural language model for word embeddings. The results obtained are compared with the baseline AttnGAN model and conduct various analyses on incorporating RoBERTa text encoder concerning simple encoder in the existing system. Various performance improvements were noted compared to baseline Attention Generative networks. The FID score has decreased from 23.98 in AttnGAN to 20.77 with incorporation of RoBERTa model with AttnGAN.