Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN
Generative adversarial networks (GANs) are often used to generate realistic images, and GANs are effective in fitting high-dimensional probability distributions. However, during training, they often produce model collapse, which is the inability of the generative model to map the input noise to the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/11/5382 |