title: DCGANs: discriminator getting too strong too quickly to allow generator to learn
body: <p>I am trying to use <a href="https://github.com/carpedm20/DCGAN-tensorflow/blob/master/model.py" rel="nofollow noreferrer">this version of the DCGAN code</a> (implemented in Tensorflow) with some of my data. I run into the problem of the discriminator becoming too strong way too quickly for generator to learn anything.</p>
  
  <p>Now there are some tricks typically recommended for that problem with GANs:</p>
  
  <ul>
  <li><p>batch normalisation (already there in DCGANs code)</p></li>
  <li><p>giving a head start to generator.</p></li>
  </ul>
  
  <p>I did some version of the latter by allowing 10 iterations of generator per 1 of discriminator (not just in the beginning, but throughout the entire training), and that's how it looks:
  <a href="https://i.stack.imgur.com/rNI4P.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/rNI4P.png" alt="Loss functions of generator and discriminator"></a></p>
  
  <p>Adding more generator iterations in this case helps only by slowing down the inevitable - discriminator growing too strong and suppressing the generator learning. </p>
  
  <p>Hence I would like to ask for an advice on whether there is another way that could help the problem of a too strong discriminator?</p>