基于词嵌入操作的条件生成对抗网络用于表情符号合成
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation
AI总结：
本文提出一种优化训练的深度卷积GAN，通过整合Google的word2vec词嵌入，生成高度逼真、与真实表情符号几乎相同的合成表情符号。
摘要：
表情符号已成为日常数字通信中非常流行的一部分。其吸引力部分源于其比纯文本更能微妙而细致地捕捉和激发情感的能力。在深度学习领域近期进展的背景下，生成对抗网络（GANs）在图像生成方面有广泛的应用和影响。在本文中，我们提出了一种深度卷积GAN（DC-GAN）的新应用，并采用优化的训练过程。我们展示通过将Google的word2vec模型中的词嵌入条件整合到网络中，生成器能够合成高度逼真的表情符号，几乎与真实的表情符号相同。
英文摘要：
Emojis have become a very popular part of daily digital communication. Their appeal comes largely in part due to their ability to capture and elicit emotions in a more subtle and nuanced way than just plain text is able to. In line with recent advances in the field of deep learning, there are far reaching implications and applications that generative adversarial networks (GANs) can have for image generation. In this paper, we present a novel application of deep convolutional GANs (DC-GANs) with an optimized training procedure. We show that via incorporation of word embeddings conditioned on Google's word2vec model into the network, the generator is able to synthesize highly realistic emojis that are virtually identical to the real ones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
