# Labeling Topics with Images using Neural Networks

arXiv: https://arxiv.org/abs/1608.00470 · HTML: https://mlsanity.com/paper/1608.00470/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics](https://mlsanity.com/paper/2108.12822/) (2021)
- [Labeled Interactive Topic Models](https://mlsanity.com/paper/2311.09438/) (2023)

## Abstract (arXiv)

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.