Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE

Understand

We propose a number of new algorithms for learning deep energy models and demonstrate their properties.

• We show that our SteinCD performs well in term of test likelihood, while SteinGAN performs well in terms of generating realistic looking images.
• Our results suggest promising directions for learning better models by combining GAN-style methods with traditional energy-based learning.