DCGAN:Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks论文
本文提出了一种叫做深度卷积生成对抗网络的CNN，它具有一定的结构约束，展示了其在非监督学习方向上强有力的候选方案。通过在各种各样的图像数据集的训练，我们展示了令人信服的证据，我们的深度卷积对抗对（adversarial pair）从对象到场景在生成模型和判别模型上都能够学习到层级的表示。此外，我们在一些的新的任务上使用学习到的特征表明它们一般的图像表示上具有通用性。 模型方法 全卷积网络（Spri
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相关文章
- 1. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 2. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- 3. 论文阅读——《Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks》
- 4. 阅读论文 GraphGAN: Graph Representation Learning with Generative Adversarial Nets
- 5. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 6. 论文译文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 7. Deep Semantic Hashing with Generative Adversarial Networks 论文review（翻译）
- 8. 生成对抗网络学习笔记3----论文unsupervised representation learning with deep convolutional generative adversarial
- 9. 论文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
- 10. 论文学习:Learning to Generate Time-Lapse Videos Using Multi-StageDynamic Generative Adversarial Networks