文獻 Multiview Generative Adversarial Network and Its Application in Pearl Classification閱讀心得——2
這篇文章的主要工作 1） 首先，我們提出MV-GAN作爲一種新的深度學習框架來產生珍珠的多視圖圖像。特別是，這裏我們將每個珍珠的多視圖圖像按上、左、右、主視圖和稀有視圖的順序疊加到圖像通道維度中，形成多視圖輸入數據。 2） 其次，MV-GAN框架結合了DCGAN的網絡結構和CGAN的標籤訓練方式。因此，鑑別器有四個卷積層，發生器有四個反捲積層。同時，珍珠標籤被用來限制每一層的訓練和生成圖像的自由度
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相關文章
- 1. Generative Adversarial Networks: An Overview文獻閱讀筆記
- 2. 論文閱讀——《Generative Adversarial Nets》
- 3. Finding Tiny Faces in the Wild with Generative Adversarial Network
- 4. 【閱讀筆記】Generative Adversarial Nets
- 5. 【文獻閱讀】Perceptual Generative Adversarial Networks for Small Object Detection –CVPR-2017
- 6. 閱讀筆記 Modality-specific and shared generative adversarial network for cross-modal retrieval
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- 8. [Style Transfer]Generative Adversarial Network in Medical Imaging: A Review
- 9. 論文閱讀——《Conditional Generative Adversarial Nets》
- 10. 文獻閱讀報告 - Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks...