Name
Title | ||
|---|---|---|
Generative adversarial networks with mixture of t-distributions noise for diverse image generation. |
Abstract | ||
|---|---|---|
Image generation is a long-standing problem in the machine learning and computer vision areas. In order to generate images with high diversity, we propose a novel model called generative adversarial networks with mixture of t-distributions noise (tGANs). In tGANs, the latent generative space is formulated using a mixture of t-distributions. Particularly, the parameters of the components in the mixture of t-distributions can be learned along with others in the model. To improve the diversity of the generated images in each class, each noise vector and a class codeword are concatenated as the input of the generator of tGANs. In addition, a classification loss is added to both the generator and the discriminator losses to strengthen their performances. We have conducted extensive experiments to compare tGANs with a state-of-the-art pixel by pixel image generation approach, pixelCNN, and related GAN-based models. The experimental results and statistical comparisons demonstrate that tGANs perform significantly better than pixleCNN and related GAN-based models for diverse image generation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neunet.2019.11.003 | Neural Networks |
Keywords | Field | DocType |
Image generation,Generate adversarial networks,Diversity,Mixture of t-distributions,Class codeword | Image generation,Discriminator,Pattern recognition,Concatenation,Pixel,Artificial intelligence,Code word,Generative grammar,Mathematics,Machine learning,Adversarial system | Journal |
Volume | Issue | ISSN |
122 | 1 | 0893-6080 |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinxuan Sun | 1 | 3 | 1.40 |
| Guoqiang Zhong | 2 | 16 | 3.78 |
| Yang Chen | 3 | 4 | 1.44 |
| Yongbin Liu | 4 | 3 | 0.73 |
| Tao Li | 5 | 387 | 41.20 |
| Kaizhu Huang | 6 | 1010 | 83.94 |