Labeling Topics with Images using Neural Networks

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(n2) for the current state-of-the-art method, and is also significantly more accurate.