  Text-to-image synthesis is the task of generating images from text
descriptions. Image generation, by itself, is a challenging task. When we
combine image generation and text, we bring complexity to a new level: we need
to combine data from two different modalities. Most of recent works in
text-to-image synthesis follow a similar approach when it comes to neural
architectures. Due to aforementioned difficulties, plus the inherent difficulty
of training GANs at high resolutions, most methods have adopted a multi-stage
training strategy. In this paper we shift the architectural paradigm currently
used in text-to-image methods and show that an effective neural architecture
can achieve state-of-the-art performance using a single stage training with a
single generator and a single discriminator. We do so by applying deep residual
networks along with a novel sentence interpolation strategy that enables
learning a smooth conditional space. Finally, our work points a new direction
for text-to-image research, which has not experimented with novel neural
architectures recently.
