# Discriminative Neural Topic Models

## TL;DR

This paper introduces a neural network-based topic modeling method that efficiently learns from text and image data, supporting streaming and scalable processing without assuming specific feature distributions.

## Contribution

It presents a novel neural approach for joint text and image topic modeling that is online, flexible, and easily scalable on GPU hardware.

## Key findings

- Supports both text and image data for topic modeling
- Enables online and streaming data processing
- Easily scalable with GPU implementation

## Abstract

We propose a neural network based approach for learning topics from text and image datasets. The model makes no assumptions about the conditional distribution of the observed features given the latent topics. This allows us to perform topic modelling efficiently using sentences of documents and patches of images as observed features, rather than limiting ourselves to words. Moreover, the proposed approach is online, and hence can be used for streaming data. Furthermore, since the approach utilizes neural networks, it can be implemented on GPU with ease, and hence it is very scalable.