  A commonly used evaluation metric for text-to-image synthesis is the
Inception score (IS) \cite{inceptionscore}, which has been shown to be a
quality metric that correlates well with human judgment. However, IS does not
reveal properties of the generated images indicating the ability of a
text-to-image synthesis method to correctly convey semantics of the input text
descriptions. In this paper, we introduce an evaluation metric and a visual
evaluation method allowing for the simultaneous estimation of the realism,
variety and semantic accuracy of generated images. The proposed method uses a
pre-trained Inception network \cite{inceptionnet} to produce high dimensional
representations for both real and generated images. These image representations
are then visualized in a $2$-dimensional feature space defined by the
t-distributed Stochastic Neighbor Embedding (t-SNE) \cite{tsne}. Visual
concepts are determined by clustering the real image representations, and are
subsequently used to evaluate the similarity of the generated images to the
real ones by classifying them to the closest visual concept. The resulting
classification accuracy is shown to be a effective gauge for the semantic
accuracy of text-to-image synthesis methods.
