eal outputs</span> (say, a database of celebrities). The hope is that as the two networks face off, they'll both get better and better&mdash;with the end result being a generator network that produces realistic outputs.
    </p>
    
    <p>      
      To sum up: <strong><em>Generative adversarial networks</em></strong> are neural networks that learn to choose samples from a special distribution (the <em>"generative"</em> part of the name), and they do this by setting up a competition (hence <em>"adversari