| method | supervised | multi-domain | multi-modal |
|---|---|---|---|
| pix2pix [1] | yes | no | no |
| BicycleGAN [6], [7] | yes | no | yes |
| [10] | yes | yes | yes |
| cycleGAN [2], UNIT [3] | no | no | no |
| MUNIT [4], AugCGAN [5] | no | no | yes |
| starGAN [8], [9], [11], [12], ComboGAN [13], [14] | no | yes | no |
| SMIT[15], DRIT++[16], starGANv2[19] | no | yes | yes |
Exemplar-guided domain translation: use an exemplar to define the target domain [17] [18]
Reference
[1] Image-to-Image Translation with Conditional Adversarial Networks
[2] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
[3] Unsupervised image-to-image translation networks
[4] Multimodal Unsupervised Image-to-Image Translation
[5] Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
[6] Toward Multimodal Image-to-Image Translation
[7] Image-to-image translation for cross-domain disentanglement
[8] StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
[9] Unsupervised Multi-Domain Image Translation with Domain-specific Encoders/Decoders
[10] Multi-view image Generation from a single-view
[11] Show, Attend and Translate- Unpaired Multi-Domain Image-to-Image Translation with Visual Attention
[12] Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
[13] ComboGAN: Unrestrained Scalability for Image Domain Translation
[14] A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
[15] SMIT: Stochastic Multi-Label Image-to-Image Translation
[16] DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
[17] Cross-domain Correspondence Learning for Exemplar-based Image Translation
[18] High-Resolution Daytime Translation Without Domain Labels
[19] StarGAN v2: Diverse Image Synthesis for Multiple Domains