matlab光照模型代码-InfoGAN:通过信息最大化生成对抗网络的可解释表示学习

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matlab光照模型代码InfoGAN InfoGAN体系结构 Tensorlayer的实现。 结果 MNIST 操纵第一个连续潜在代码 更改将旋转数字: 操纵第二个连续潜在代码 更改将更改数字的宽度: 操纵离散潜在代码(分类) 更改将更改数字的类型: 随机生成和损失图 G_loss在经过足够的迭代次数后稳步增加,这表明鉴别器越来越强,并且表明训练结束。 西莉亚 操纵离散潜在代码 方位角(姿势): 有无眼镜: 发色: 发量: 灯光: 面Kong 损失图 方位角 随机生成 椅子 回转 跑步 MNIST 开始使用python train.py训练; 这将自动下载数据集。 要查看结果,请执行python test.py并输入已保存模型的编号。 西莉亚 在config.py设置图像文件夹。 数据集的一些链接: 开始训练。 python train.py 面Kong 在config.py设置您的数据文件夹。 BFM 2009的链接: 。 在生成数据之前,应先下载该文件。 使用data_generator的代码生成数据。 在MATLAB中调用gen_data 。 开始使用python train.

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