DCGAN:Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks論文
本文提出了一種叫做深度卷積生成對抗網絡的CNN，它具有一定的結構約束，展示了其在非監督學習方向上強有力的候選方案。通過在各種各樣的圖像數據集的訓練，我們展示了令人信服的證據，我們的深度卷積對抗對（adversarial pair）從對象到場景在生成模型和判別模型上都能夠學習到層級的表示。此外，我們在一些的新的任務上使用學習到的特徵表明它們一般的圖像表示上具有通用性。 模型方法 全卷積網絡（Spri
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相關文章
- 1. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 2. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- 3. 論文閱讀——《Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks》
- 4. 閱讀論文 GraphGAN: Graph Representation Learning with Generative Adversarial Nets
- 5. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 6. 論文譯文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 7. Deep Semantic Hashing with Generative Adversarial Networks 論文review（翻譯）
- 8. 生成對抗網絡學習筆記3----論文unsupervised representation learning with deep convolutional generative adversarial
- 9. 論文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
- 10. 論文學習:Learning to Generate Time-Lapse Videos Using Multi-StageDynamic Generative Adversarial Networks