Name
Abstract | ||
|---|---|---|
Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/a11010004 | ALGORITHMS |
Keywords | Field | DocType |
deep neural network,sketch,arts synthesis,style transfer | Pattern recognition,Convolution,Painting,Artificial intelligence,Pixel,Generative grammar,Deep learning,Universality (philosophy),Artificial neural network,Machine learning,Mathematics,Sketch | Journal |
Volume | Issue | Citations |
11 | 1 | 1 |
PageRank | References | Authors |
0.36 | 5 | 5 |