GAS-Net: Generative Artistic Style Neural Networks for Fonts
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Generating new fonts is a time-consuming and labor-intensive, especially in a language with a huge amount of characters like Chinese. Various deep learning models have demonstrated the ability to efficiently generate new fonts with a few reference characters of that style. This project aims to develop a few-shot cross-lingual font generator based on AGIS-Net and improve the performance metrics mentioned. Our approaches include redesigning the encoder and the loss function. We will validate our method on multiple languages and datasets mentioned.
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