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Simon4Yan avatar Simon4Yan commented on May 23, 2024

感谢关注。eSPGAN 直接用额外的分类器的损失来约束生成器，也就是生成器生成的样本，需要能够被分类器分对，也就是和identity有关的信息能够保留。这种方式和SPGAN的方式还不太一样。

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yyang-2018 avatar yyang-2018 commented on May 23, 2024

感谢您的回复，我现在的理解是，就是将原来孪生网络保证相似性换成一个分类器，这个分类器类似于基准网络训练时的二分类判别做法，换句话说，就是使用分类器来约束，是要比孪生网络效果好一点？虽然我测试下来两个结果差不多，期待您的再次回复！

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Simon4Yan avatar Simon4Yan commented on May 23, 2024

你说的这个做法和eSPGAN有啥本质区别吗？不都是一样的方式吗？

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yyang-2018 avatar yyang-2018 commented on May 23, 2024

嗯嗯，本质上是一样的，我之前不是很理解espgan和spgan的区别，现在明白了，谢谢您的回复！

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