GAN Theory
Modifyingthe Optimization of GAN
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题目 |
内容 |
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GAN |
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DCGAN |
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WGAN |
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Least-square GAN |
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Loss Sensitive GAN |
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Energy-based GAN |
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Boundary-seeking GAN |
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Unroll GAN |
Different Structure from the Original GAN
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题目 |
内容 |
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Conditional GAN |
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Semi-supervised GAN |
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InfoGAN |
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BiGAN |
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Cycle GAN |
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Disco GAN |
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VAE-GAN |
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LAPGAN |
用了多个GAN可生成高分辨率图像 |
GAN Application
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pix2pix |
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题目 |
内容 |
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Image-to-Image Translation with Conditional Adversarial Networks |
image2image、paired Image-to-Image Translation |
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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs |
image2imageHD、paired Image-to-Image Translation |
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CycleGAN |
Unpaired Image-to-Image Translation |
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Disco GAN |
侧重分析双向映射，或者说 bijective mapping 的约束：避免 mode collapse 进而提升生成样本质量的 |
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DualGAN |
生成器和判别器都和pix2pix一样。 用了wgan来训练。 |
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注：最后三篇论文的想法十分相似，几乎可以说是孪生三兄弟 |
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text2image |
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题目 |
内容 |
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人脸生成 |
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题目 |
内容 |
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Face-generator - Generate human faces with neural networks |
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Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis |
根据单一侧脸生成正面逼真人脸 |
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NEURAL FACE |
use DCGAN、链接：https://carpedm20.github.io/faces/ |
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注：DCGAN、WGAN这类都可以生成人脸 |
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按生成的图片种类分 |
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题目 |
内容 |
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生成卧室 |
DCGAN、WGAN |
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生成动漫头像 |
DCGAN |