  Prior works about text-to-image synthesis typically concatenated the sentence
embedding with the noise vector, while the sentence embedding and the noise
vector are two different factors, which control the different aspects of the
generation. Simply concatenating them will entangle the latent factors and
encumber the generative model.
  In this paper, we attempt to decompose these two factors and propose Factor
Decomposed Generative Adversarial Networks~(FDGAN). To achieve this, we firstly
generate images from the noise vector and then apply the sentence embedding in
the normalization layer for both generator and discriminators. We also design
an additive norm layer to align and fuse the text-image features. The
experimental results show that decomposing the noise and the sentence embedding
can disentangle latent factors in text-to-image synthesis, and make the
generative model more efficient. Compared with the baseline, FDGAN can achieve
better performance, while fewer parameters are used.

Multimedia;Machine Learning