检查您的理解情况:GAN 解剖学
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
判断正误:判别器网络和生成器网络仅通过生成器生成的数据和判别器生成的标签相互影响。在反向传播方面,它们是单独的网络。
正确
错误:在生成器训练期间,梯度会通过判别器网络传播到生成器网络(尽管判别器不会在生成器训练期间更新其权重)。
错误
正确答案:在生成器训练期间,梯度会通过判别器网络传播到生成器网络(尽管判别器不会在生成器训练期间更新其权重)。因此,分类器网络中的权重会影响生成器网络的更新。
判断正误:典型的 GAN 会同时训练生成器和鉴别器。
正确
错误。典型的 GAN 会在训练鉴别器和训练生成器之间交替。有一些 [研究](https://arxiv.org/abs/1706.04156) 探讨了如何同时训练生成器和鉴别器。
错误
正确。典型的 GAN 会在训练鉴别器和训练生成器之间交替。
判断正误:GAN 在训练分类器和生成器时始终使用相同的损失函数。
正确
错误。虽然 GAN 可以为生成器和鉴别器训练使用相同的损失函数(或仅在符号上不同的相同损失函数),但这并不是必需的。事实上,更常见的是为鉴别器和生成器使用不同的损失函数。
错误
正确。虽然 GAN 可以为生成器和鉴别器训练使用相同的损失函数(或仅在符号上不同的相同损失函数),但这并不是必需的。事实上,更常见的是为鉴别器和生成器使用不同的损失函数。
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最后更新时间 (UTC):2025-02-26。
[null,null,["最后更新时间 (UTC):2025-02-26。"],[[["\u003cp\u003eDuring generator training, gradients propagate through the discriminator to the generator, influencing its updates.\u003c/p\u003e\n"],["\u003cp\u003eA typical GAN alternates between training the discriminator and training the generator, rather than simultaneous training.\u003c/p\u003e\n"],["\u003cp\u003eGANs often employ different loss functions for the discriminator and generator, optimizing each network separately.\u003c/p\u003e\n"]]],[],null,["# Check Your Understanding: GAN Anatomy\n\n\u003cbr /\u003e\n\nTrue or false: the discriminator network and generator network influence each other solely through the data produced by the generator and the labels produced by the discriminator. When it comes to backpropagation, they are separate networks. \nTrue \nIncorrect: during generator training, gradients propagate through the discriminator network to the generator network (although the discriminator does not update its weights during generator training). \nFalse \nCorrect: during generator training, gradients propagate through the discriminator network to the generator network (although the discriminator does not update its weights during generator training). So the weights in the discriminator network influence the updates to the generator network. \nTrue or false: a typical GAN trains the generator and the discriminator simultaneously. \nTrue \nIncorrect. A typical GAN alternates between training the discriminator and training the generator. There is some \\[research \\](https://arxiv.org/abs/1706.04156) on training the generator and discriminator simultaneously. \nFalse \nCorrect. A typical GAN alternates between training the discriminator and training the generator. \nTrue or false: a GAN always uses the same loss function for both discriminator and generator training. \nTrue \nIncorrect. While it's possible for a GAN to use the same loss for both generator and discriminator training (or the same loss differing only in sign), it's not required. In fact it's more common to use different losses for the discriminator and the generator. \nFalse \nCorrect. While it's possible for a GAN to use the same loss for both generator and discriminator training (or the same loss differing only in sign), it's not required. In fact it's more common to use different losses for the discriminator and the generator."]]