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StarGAN

Ref:
https://github.com/yunjey/stargan
https://arxiv.org/pdf/1711.09020.pdf

 StarGAN如圖(b)與其他GAN模型相較, 受到關注的特色在於其不同Domain間的轉換可以使用同一個模型, 不需要一對一Domain的產生很多組模型, 如圖(a).
Picture originated from: https://arxiv.org/pdf/1711.09020.pdf






下圖是使用StarGAN生成的圖片, 一組有六張, 分別是
Input + Black_Hair +Golden_Hair +Brown_Hair +Gender_Change +Aged 共5個Domain

Train這個模型在FX705GE(CPU:INTEL i7-8750H, 32G RAM)上花了約39小時作Training, 總共200,000 steps, 執行速度7 sec/ 10steps ,
相對GL753VE(CPU:INTEL i7-7700HQ, 24G RAM) 90 sec/ 10steps <== 只是worker node, 未開啟NVIDIA CUDA

從下列image看來效果有好有壞, 視人頭比例, training dataset, 相片品質及背景等條件而異.
感謝同仁們(含EX-)及致中提供玉照協助!! 使用celebrity dataset作training 及部份testing



        Input               + Black_Hair       + Golden_Hair    + Brown_Hair       + Gender_Change + Aged






               Input            + Black_Hair       + Golden_Hair     + Brown_Hair     + Gender_Change + Aged





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