Objective function

The objective function is the main method for training a 3D-GAN. It provides loss values, which are used to calculate gradients and then to update the weight values. The adversarial loss function for a 3D-GAN is as follows:

Here, log(D(x)) is the binary cross-entropy loss or classification loss, log(1-D(G(z))) is the adversarial loss, z is the latent vector from probabilistic space p(z), D(x) is the output from the discriminator network, and G(z) is the output from the generator network.