Can I read JEM++: Improved Techniques for Training JEM on EtoBox?

JEM++: Improved Techniques for Training JEM by Yang, Xiulong; Ji, Shihao is a scholarly article available to read on EtoBox.

What is JEM++: Improved Techniques for Training JEM about?

Joint Energy-based Model (JEM) is a recently proposed hybrid model that retains strong discriminative power of modern CNN classifiers, while generating samples rivaling the quality of GAN-based approaches. In this paper, we propose a variety of new training procedures and architecture features to improve JEM's accuracy, training stability, and speed altogether. 1) We propose a proximal SGLD to generate samples in the proximity of samples from the previous step, which improves the stability. 2) We further treat the approximate maximum likelihood learning of EBM as a multi-step differential game, and extend the YOPO framework to cut out redundant calculations during backpropagation, which accelerates the training substantially. 3) Rather than initializing SGLD chain from random noise, we introduce a new informative initialization that samples from a distribution estimated from training data. 4) This informative initialization allows us to enable batch normalization in JEM, which further releases the power of modern CNN architectures for hybrid modeling. Code: https://github.com/sndnyang/JEMPP

Author

Yang, Xiulong; Ji, Shihao

Published

2021

Language

EN