Generative Adversarial Networks (GAN) and Autoencoders are the ones that come to mind. There are also all the models used in research leading up to the "deep learning" craze: Helmholtz machine, Boltzman machine, Deep belief networks.

http://pyro.ai/examples/vae.html

http://www.scholarpedia.org/article/Boltzmann_machine

http://www.scholarpedia.org/article/Deep_belief_networks

Geoff Hinton's Coursera course goes pretty deep on the unsupervised models

https://www.coursera.org/learn/neural-networks

Reinforcement learning probably counts too (Deep Q Learning, Policy Gradient, Actor-Critic networks might be equivalent to GANs?)

http://pytorch.org/tutorials/intermediate/reinforcement_q_le...

https://github.com/vy007vikas/PyTorch-ActorCriticRL