  Topics generated by topic models are usually represented by lists of $t$
terms or alternatively using short phrases and images. The current
state-of-the-art work on labeling topics using images selects images by
re-ranking a small set of candidates for a given topic. In this paper, we
present a more generic method that can estimate the degree of association
between any arbitrary pair of an unseen topic and image using a deep neural
network. Our method has better runtime performance $O(n)$ compared to $O(n^2)$
for the current state-of-the-art method, and is also significantly more
accurate.
