生成对抗网络研究进展

Vishnu B. Raj, K. Hareesh
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引用次数: 7

摘要

最近,监督学习被广泛应用于计算机视觉。但无监督学习得到的关注较少。cnn的一个分支被分类为生成对抗网络(GANs),它有一些架构上的限制,并且表现出它们是无监督学习的有力竞争者。在不同的图像数据集上进行训练,它显示了确凿的证据,证明对抗性配对在鉴别器和生成器中都学习了从零件到场景的描绘层次。此外,学习到的特征可以用于各种创新任务,表明它们适合作为一般图像表示。
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Review on Generative Adversarial Networks
Lately, supervised learning is hugely adopted in computer vision. But unsupervised learning has earned less consideration. A branch of CNNs classified as generative adversarial networks (GANs) is made acquainted, it has some architectural restraints, and exhibit that they are a tough contender for unsupervised learning. Training on different datasets of images, it displays conclusive proof that the adversarial pair learns a hierarchy of portrayal from parts to scenes in both the discriminator and generator. Also, the learned features can be used for variety of innovative tasks, indicating their appropriateness as general image representation.
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