Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545419 | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
DocType | Volume | ISSN |
Conference | abs/1806.03027 | 1051-4651 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
4 | ||