# Generating Image Sequence from Description with LSTM Conditional GAN

arXiv: https://arxiv.org/abs/1806.03027 · HTML: https://mlsanity.com/paper/1806.03027/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset](https://mlsanity.com/paper/2302.13855/) (2023)

## Abstract (arXiv)

Generating images from word descriptions is a challenging task. Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects. In this paper, we propose a new neural network architecture of LSTM Conditional Generative Adversarial Networks to generate images of real-life objects. Our proposed model is trained on the Oxford-102 Flowers and Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model produces the better results surpassing other state-of-art approaches.