  In this paper, we present the result of adopting skip connections and dense
layers, previously used in image classification tasks, in the Fisher GAN
implementation. We have experimented with different numbers of layers and
inserting these connections in different sections of the network. Our findings
suggests that networks implemented with the connections produce better images
than the baseline, and the number of connections added has only slight effect
on the result.
