Advantages

• No need for Markov chains: GANs do not require Markov chains for sampling or training.
• Sharp distributions: They handle sharp, degenerate distributions well compared to Markov chain methods.
• Simple training: Only backpropagation is used to obtain gradients.
• No inference: No statistical inference is needed during learning.

Disadvantages

• No explicit density: There is no explicit representation of the generative distribution $p_g(x)$.
• Training imbalance: It is necessary to synchronize the models carefully to avoid over-training the generative model without updating the discriminative model.