论文标题
关于CNN实现图像到图像语言转换的能力
On the Ability of a CNN to Realize Image-to-Image Language Conversion
论文作者
论文摘要
本文的目的是揭示卷积神经网络（CNN）对图像到图像语言转换的新任务的能力。我们提出了一个新的网络来解决此任务，通过将韩国挂hangul字符的图像直接转换为相当于语音拉丁字符的图像。未明确提供Hangul和语音符号之间的转换规则。提出的网络的结果表明，可以执行图像到图像语言转换。此外，它表明，即使从有限的学习数据中，它也可以掌握hangul的结构特征。此外，它引入了一个新的网络，以便在输入和输出具有显着不同的功能时使用。
The purpose of this paper is to reveal the ability that Convolutional Neural Networks (CNN) have on the novel task of image-to-image language conversion. We propose a new network to tackle this task by converting images of Korean Hangul characters directly into images of the phonetic Latin character equivalent. The conversion rules between Hangul and the phonetic symbols are not explicitly provided. The results of the proposed network show that it is possible to perform image-to-image language conversion. Moreover, it shows that it can grasp the structural features of Hangul even from limited learning data. In addition, it introduces a new network to use when the input and output have significantly different features.
