,    Gaussian noise, weight norm   -----------------------------  \  [c|c|c]{} **Generator G**& **Classifier C**& **Discriminator D (Good GAN only)**\ Input Label $y$, Noise $z$ & Input $00 \times 00$ Colored Image & Input $00 \times 00$ Colored Image, Label $y$\  [@c@]{}MLP 0000 units,\ Relu, batch norm\ Reshape $000 \times 0 \times 0$\ \ \ $0 \times 0$ deconv. 000. stride 0,\ Relu, batch norm  &    -------------------------------------------           Gaussian noise, 0.0 dropout    $0 \times 0$ conv. 00. lRelu, weight norm    $0 \times 0$ conv. 00. lRelu, weight norm          $0 \times 0$ conv. 00. lRelu,              stride 0, weight norm                   0.0 dropout   -------------------------------------------  &    -------------------------------------------                   0.0 dropout    $0 \times 0$ conv. 00. lRelu, weight norm          $0 \times 0$ conv. 00. lRelu,              stride 0, weight norm                   0.0 dropout   -------------------------------------------  \    -------------------------------------    $0 \times 0$ deconv. 000. stride 0,             Relu, batch norm   -------------------------------------  &    --------------------------------------------    $0 \times 0$ conv. 000. lRelu, weight norm    $0 \times 0$ conv. 000. lRelu, weight norm          $0 \times 0$ conv. 000. lRelu,              stride 0, weight norm                   0.0 dropout   --------------------------------------------  &    -------------------------------------------    $0 \times 0$ conv. 00. lRelu, weight norm          $0 \times 0$ conv. 00. lRelu,              stride 0, weight norm                   0.0 dropout   -------------------------------------------  \    -----------------------------------    $0 \times 0$ deconv. 0. stride 0,          sigmoid, weight norm   -----------------------------------  &  [@c@]{}$0 \times 0$ conv. 000. lRelu, weight norm\ $0 \times 0$ conv. 000. lRelu, weight norm\ $0 \times 0$ conv. 000. lRelu, weight norm\ \ Global pool\ MLP 00 units, softmax, weight norm  &  [@c@]{}$0 \times 0$ conv. 000. lRelu, weight norm\ $0 \times 0$ conv. 000. lRelu, weight norm\ \ Global pool\ MLP 0 unit, sigmoid, weight norm  \  [c|c|c]{} **Generator G**& **Classifier C**& **Discriminator D (Good GAN only)**\ Input Label $y$, Noise $z$ & Input $00 \times 00$ Colored Image & Input $00 \times 00$ Colored Image, Label $y$\  [@c@]{}MLP 0000 units,\ Relu, batch norm\ Reshape $000 \times 0 \times 0$\ \ \ $0 \times 0$ deconv. 000. stride 0,\ Relu, batch norm  &    -------------------------------------------           Gaussian noise, 0.0 dropout    $0 \times 0$ conv. 00. lRelu, weight norm    $0 \times 0$ conv. 00. lRelu, weight norm          $0 \times 0$ conv. 00. lRelu,              stride 0, weight norm                   0.0 dropout   -------------------------------------------  &    -------------------------------------------                   0.0 dropout    $0 \times 0$ conv. 00. lRelu, weight norm          $0 \times 0$ conv. 00. lRelu,              stride 0, weight norm                   0.0 dropout   -------------------------------------------  \    -------------------------------------    $0 \times 0$ deconv. 000. stride 0,             Relu, batch norm   -------------------------------------  &    --------------------------------------------    $0 \times 0$ conv. 000. lRelu, weight norm    $0 \times 0$ conv. 000. lRelu, weight norm          $0 \times 0$ conv. 000. lRelu,              stride 0, weight norm                   0.0 dropout   --------------------------------------------  &    -------------------------------------------    $0 \times 0$ conv. 00. lRelu, weight norm          $0 \times 0$ conv. 00. lRelu,              stride 0, weight norm                   0.0 dropout   -------------------------------------------  \    -----------------------------------    $0 \times 0$ deconv. 0. stride 0,          sigmoid, weight norm   -----------------------------------  &  [@c@]{}$0 \times 0$ conv. 000. lRelu, weight norm\ $0 \times 0$ conv. 000. lRelu, weight n