论文标题
使用深度可分离卷积和超级分辨率生成对抗网络对进行性生成对抗网络进行时间效率培训
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network
论文作者
论文摘要
生成对抗网络已成功地用于生成大小1024^2的高分辨率增强图像。尽管生成的增强图像是前所未有的，但模型的训练时间异常高。传统的gan需要培训歧视器和发电机。在Progressive Gan中，这是当前用于图像增强的最新gan，而不是立即训练GAN，这是同时提出歧视者和发电机增长的新概念。尽管较低的阶段（例如4x4和8x8火车）很快，但后期阶段却浪费了大量的时间，这可能需要几天的时间才能完成模型培训。在我们的论文中，我们提出了一种新型的管道，将渐进式甘恩与轻微的修改和超级分辨率gan结合在一起。超级分辨率将低分辨率图像示例为高分辨率图像，这些图像可能被证明是一种有用的资源，可以指数缩短训练时间。
Generative Adversarial Networks have been employed successfully to generate high-resolution augmented images of size 1024^2. Although the augmented images generated are unprecedented, the training time of the model is exceptionally high. Conventional GAN requires training of both Discriminator as well as the Generator. In Progressive GAN, which is the current state-of-the-art GAN for image augmentation, instead of training the GAN all at once, a new concept of progressing growing of Discriminator and Generator simultaneously, was proposed. Although the lower stages such as 4x4 and 8x8 train rather quickly, the later stages consume a tremendous amount of time which could take days to finish the model training. In our paper, we propose a novel pipeline that combines Progressive GAN with slight modifications and Super Resolution GAN. Super Resolution GAN up samples low-resolution images to high-resolution images which can prove to be a useful resource to reduce the training time exponentially.
