[解读] ClusterGAN : Latent Space Clustering in Generative Adversarial Networks

时间 2020-12-23

标签深度学习机器学习繁體版

原文原文链接

链接: https://arxiv.org/abs/1809.03627v2 ClusterGAN 是一个 GAN 的变体, 通过使得生成样本反推的隐变量在隐空间中具有明显的簇的特征, 来提升 GAN 的性能. 相关工作 InfoGAN [4] 是一个研究在隐空间中进行可解释性的表征学习的开创新方法, 目标是创建可解释性的和可解的隐变量. 虽然 InfoGAN 使用了离散的隐变量, 但它不是为聚类

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1. Graphical Generative Adversarial Networks
2. [解读] Coupled Generative Adversarial Networks
3. Triangle Generative Adversarial Networks
4. Contrastive Generative Adversarial Networks
5. A wizard’s guide to Adversarial Autoencoders: Part 2, Exploring latent space with Adversarial Autoen
6. [Style Transfer]—Generative adversarial networks and adversarial method in biomedical image analysis
7. Self-Attention Generative Adversarial Networks 论文解读
8. Generative Adversarial Networks: An Overview文献阅读笔记
9. [解读] COEGAN Evaluating the Coevolution Effect in Generative Adversarial Networks
10. Generative Adversarial Networks overview（2）

>>更多相关文章<<