Name
Abstract | ||
|---|---|---|
In this work, we focus on building style transfer, which transforms ruin or old buildings to modern architecture. Inspired by Gaty's and Goodfellow's style transfer and generative adversarial network (GAN), we use CycleGAN to conquer this type of problem. As we know, image style transfer usually generated unexpected artifacts. To avoid the artifacts and generate better images, we add so called “perception loss” into the network, which is the feature loss extracted by VGG pre-trained model. In the part of “cycle” structure, we adjust cycle loss by changing the ratio of weighting parameters. Finally, we collect images of both ruin (or old) and modern architecture from websites and use unsupervised learning to train the model. The experimental results show our proposed method indeed realize the modern architecture style transfer for ruin or old buildings. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISCAS.2019.8702121 | 2019 IEEE International Symposium on Circuits and Systems (ISCAS) |
Keywords | Field | DocType |
Generators,Architecture,Feature extraction,Image color analysis,Training,Buildings,Computer architecture | Architecture,Weighting,Generative adversarial network,Computer science,Electronic engineering,Feature extraction,Unsupervised learning,Artificial intelligence,Perception | Conference |
ISSN | ISBN | Citations |
0271-4302 | 978-1-7281-0397-6 | 1 |
PageRank | References | Authors |
0.38 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuan-Hsien Liu | 1 | 110 | 11.01 |
| Tsung-Jung Liu | 2 | 147 | 13.20 |
| Chia-Ching Wang | 3 | 2 | 0.76 |
| Hsin-Hua Liu | 4 | 27 | 5.68 |
| S. -C. Pei | 5 | 37 | 5.33 |