Name
Abstract | ||
|---|---|---|
Generative Adversarial Networks (GANs) are being used to learn distributions of image data. We attempt to estimate the latent vector that results in the best approximation of a real world image. We estimate the latent vector by using a metric that compares the original image and it's generated version. The existing methods minimize the error between these two images to estimate it. In our work, we also maximize the signal content in the generated image while formulating these metrics. We present several metrics based on error, signal to noise ratio and energy of the gradient image. We evaluate them by using images of t-shirts and present quantitative and qualitative results. We demonstrate an application of the proposed methods to generate new designs that are inspired by the input ones. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MLSP.2018.8517097 | 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Generative Adversarial Networks,latent vector,gradient descent,fashion designing | Gradient descent,Pattern recognition,Computer science,Signal-to-noise ratio,Artificial intelligence,Generative grammar | Conference |
ISSN | ISBN | Citations |
1551-2541 | 978-1-5386-5478-1 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arun Patro | 1 | 0 | 0.34 |
| Vishnu Makkapati | 2 | 0 | 0.34 |
| Jayanta Mukhopadhyay | 3 | 72 | 26.05 |