A path to unsupervised learning through adversarial networks

What this post added

This post details advancements in stabilizing Generative Adversarial Networks (GANs) for unsupervised learning. It introduces Laplacian Adversarial Networks (LAPGAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) as methods to improve GAN stability and enable visualization of learned features. The post also discusses the application of these techniques to video generation using Adversarial Gradient Difference Loss Predictors (AGDL), aiming to equip machines with the ability to predict future states of the world and develop common sense.