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Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation
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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.
Forward citations
Cited by 2 Pith papers
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Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction
Adding an STFT-based spectral loss to a conditional GAN improves high-frequency fidelity of generated machining force signals and, used as training augmentation, cuts surface roughness prediction MAPE from 31.4% to 8.8%.
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MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences
A GAN-based system generates Chinese meme-face images from text by conditioning on an image template, but the supporting evidence is subjective and no baseline is provided.
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