Hierarchical Adversarially Learned Inference

Understand

We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model.

• Both the generative and inference model are trained using the adversarial learning paradigm.
• We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing semantically meaningful reconstructions with different levels of fidelity.
• Furthermore, we show that minimizing the Jensen-Shanon divergence between the generative and inference network is enough to minimize the reconstruction error.