<p>From the DCGAN paper [Radford et al. <a href="https://arxiv.org/pdf/1511.06434.pdf]" rel="noreferrer">https://arxiv.org/pdf/1511.06434.pdf]</a>...</p>

<p>"The ReLU activation (Nair &amp; Hinton, 2010) is used in the generator with the exception of the output
layer which uses the Tanh function. We observed that using a bounded activation allowed the model
to learn more quickly to saturate and cover the color space of the training distribution.  Within the
discriminator we found the leaky rectified activation (Maas et al., 2013) (Xu et al., 2015) to work
well, especially for higher resolution modeling. This is in contrast to the original GAN paper, which
used the maxout activation (Goodfellow et al., 2013)."</p>

<p>It could be that the symmetry of tanh is an advantage here, since the network should be treating darker colours and lighter colours in a symmetric way. </p>
Generative adversarial networks tanh?
<p>I was wondering, why in most of the models of GAN (in MNIST at least) I saw, the activation function (for the discriminator and the generator) was tanh ?
Isn't ReLu more efficient ? (I always read that for predictive networks)</p>

<p>Thanks!</p>