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Deep Generative Adversarial Networks for Image-to-Image Translation: A Review
| Content Provider | MDPI |
|---|---|
| Author | Alotaibi, Aziz |
| Copyright Year | 2020 |
| Description | Many image processing, computer graphics, and computer vision problems can be treated as image-to-image translation tasks. Such translation entails learning to map one visual representation of a given input to another representation. Image-to-image translation with generative adversarial networks (GANs) has been intensively studied and applied to various tasks, such as multimodal image-to-image translation, super-resolution translation, object transfiguration-related translation, etc. However, image-to-image translation techniques suffer from some problems, such as mode collapse, instability, and a lack of diversity. This article provides a comprehensive overview of image-to-image translation based on GAN algorithms and its variants. It also discusses and analyzes current state-of-the-art image-to-image translation techniques that are based on multimodal and multidomain representations. Finally, open issues and future research directions utilizing reinforcement learning and three-dimensional (3D) modal translation are summarized and discussed. |
| Starting Page | 1705 |
| e-ISSN | 20738994 |
| DOI | 10.3390/sym12101705 |
| Journal | Symmetry |
| Issue Number | 10 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-16 |
| Access Restriction | Open |
| Subject Keyword | Symmetry Language Studies Image-to-image Translation Generative Adversarial Networks Adversarial Learning Deep Generative Model Deep Learning |
| Content Type | Text |
| Resource Type | Article |
