generative deep learning when trainingyou may see the adversarial loss begin to increase considerablywhile the discriminative loss tends to zero--the discriminator may end up dominating the generator if that' the casetry reducing the discriminator learning rateand increase the dropout rate of the discriminator figure play he discriminat orin each rowt wo images were dreamed up by he ganand one image comes from he training set can you ell hem apart (answerst he real images in each column are middletopbot tommiddle wrapping up gan consists of generator network coupled with discriminator network the discriminator is trained to differenciate between the output of the generator and real images from training datasetand the generator is trained to fool the discriminator remarkablythe generator nevers sees images from the training set directlythe information it has about the data comes from the discriminator gans are difficult to trainbecause training gan is dynamic process rather than simple gradient descent process with fixed loss landscape getting gan to train correctly requires using number of heuristic tricksas well as extensive tuning gans can potentially produce highly realistic images but unlike vaesthe latent space they learn doesn' have neat continuous structure and thus may not be suited for certain practical applicationssuch as image editing via latentspace concept vectors licensed to