论文标题
测量Devanagari脚本上生成对抗网络的性能
Measuring Performance of Generative Adversarial Networks on Devanagari Script
论文作者
论文摘要
遵循对抗性哲学创建生成模型的神经网络的工作是一个有趣的领域。多篇论文已经探索了建筑方面和提出的系统，并具有潜在的良好结果，但是很少有在现实世界中实现它的论文。传统上，人们将著名的MNIST数据集用作Hello，World！实施生成对抗网络（GAN）的示例。本文没有采用使用手写数字的标准路线，而是使用具有更复杂结构的Devanagari脚本。由于没有传统的方式来判断生成模型的性能，因此还建立了三个其他分类器来判断GAN模型的输出。以下论文是对实施实现的解释。
The working of neural networks following the adversarial philosophy to create a generative model is a fascinating field. Multiple papers have already explored the architectural aspect and proposed systems with potentially good results however, very few papers are available which implement it on a real-world example. Traditionally, people use the famous MNIST dataset as a Hello, World! example for implementing Generative Adversarial Networks (GAN). Instead of going the standard route of using handwritten digits, this paper uses the Devanagari script which has a more complex structure. As there is no conventional way of judging how well the generative models perform, three additional classifiers were built to judge the output of the GAN model. The following paper is an explanation of what this implementation has achieved.
