G is always really smaller than D

[kristosh]

I am training a generative adversarial network to perform style transfer from two different image domains (source and target). Since I have available class information i have an extra Q network (except G and D) that measures the classification results fo the generated images for the target domain and their labels. From the convergence of the system I have noticed that D is starting from 8 (the error of the network) and slightly drops until 4.5 and the generator error is starting from 1 and quickly drops to 0.2. Is that behaviour an example of mode-collapse? What is exactly the relationship between the errors of D and G? The loss function of D and G I am using can be found here https://github.com/r0nn13/conditional-dcgan-keras while the loss function of Q network is categorical cross-entropy. The loss functions can be found here: https://imgur.com/a/bDrTcpm