手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| CycleGAN：サイクル整合性を用いたペアなし画像間翻訳× | Generative Adversarial Network× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2017 | 2014 |
| 提唱者≠ | Jun-Yan Zhu et al. | Goodfellow, I. et al. |
| 種類≠ | Unsupervised image-to-image translation | Generative deep learning (adversarial two-network game) |
| 原典≠ | Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE International Conference on Computer Vision (ICCV), 2242–2251. DOI ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| 別名 | Cycle-Consistent Adversarial Networks, Unpaired Image-to-Image Translation, Cycle-GAN, Çevrimsel Tutarlı GAN | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| 関連≠ | 3 | 4 |
| 概要≠ | CycleGAN, introduced by Zhu et al. at ICCV 2017, learns to translate images between two visual domains without requiring paired training examples. It trains two generators and two discriminators simultaneously, enforcing a cycle-consistency constraint so that an image translated from domain X to Y and back again recovers the original. This makes it applicable whenever large aligned datasets are unavailable, such as converting photographs to artwork styles, turning summer landscapes into winter scenes, or mapping satellite imagery to map tiles. | A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation. |
| ScholarGateデータセット ↗ |
|
|