文献 Multiview Generative Adversarial Network and Its Application in Pearl Classification阅读心得——2
这篇文章的主要工作 1） 首先，我们提出MV-GAN作为一种新的深度学习框架来产生珍珠的多视图图像。特别是，这里我们将每个珍珠的多视图图像按上、左、右、主视图和稀有视图的顺序叠加到图像通道维度中，形成多视图输入数据。 2） 其次，MV-GAN框架结合了DCGAN的网络结构和CGAN的标签训练方式。因此，鉴别器有四个卷积层，发生器有四个反卷积层。同时，珍珠标签被用来限制每一层的训练和生成图像的自由度
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相关文章
- 1. Generative Adversarial Networks: An Overview文献阅读笔记
- 2. 论文阅读——《Generative Adversarial Nets》
- 3. Finding Tiny Faces in the Wild with Generative Adversarial Network
- 4. 【阅读笔记】Generative Adversarial Nets
- 5. 【文献阅读】Perceptual Generative Adversarial Networks for Small Object Detection –CVPR-2017
- 6. 阅读笔记 Modality-specific and shared generative adversarial network for cross-modal retrieval
- 7. 文献阅读: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
- 8. [Style Transfer]Generative Adversarial Network in Medical Imaging: A Review
- 9. 论文阅读——《Conditional Generative Adversarial Nets》
- 10. 文献阅读报告 - Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks...