  Emojis have become a very popular part of daily digital communication. Their
appeal comes largely in part due to their ability to capture and elicit
emotions in a more subtle and nuanced way than just plain text is able to. In
line with recent advances in the field of deep learning, there are far reaching
implications and applications that generative adversarial networks (GANs) can
have for image generation. In this paper, we present a novel application of
deep convolutional GANs (DC-GANs) with an optimized training procedure. We show
that via incorporation of word embeddings conditioned on Google's word2vec
model into the network, the generator is able to synthesize highly realistic
emojis that are virtually identical to the real ones.
