Name
Abstract | ||
|---|---|---|
In this work, we focus on building style transfer, which transforms ruin 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. To avoid the artifacts and generate better images, we add “perception loss” into the network, which is the feature loss extracted by VGG pre-trained model. We also adjust cycle loss by changing the ratio of weighting parameters. Finally, we collect images of both ruin 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 buildings. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/AICAS.2019.8771623 | 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) |
Keywords | Field | DocType |
cycle loss,generative adversarial network (GAN),modern architecture,perception loss,style transfer | Architecture,Weighting,Generative adversarial network,Computer science,Unsupervised learning,Artificial intelligence,Perception | Conference |
ISBN | Citations | PageRank |
978-1-5386-7885-5 | 1 | 0.38 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chia-Ching Wang | 1 | 2 | 0.76 |
| Hsin-Hua Liu | 2 | 27 | 5.68 |
| Soo-Chang Pei | 3 | 2054 | 241.11 |
| Kuan-Hsien Liu | 4 | 110 | 11.01 |
| Tsung-Jung Liu | 5 | 147 | 13.20 |