Authors: Sihwan Kim and Taejang Park

Affiliation: Hana Institute of Technology

Description: we present the network architecture to maximize conditional log-likelihood by optimizing the lower bound with a proper approximate posterior that has shown impressive performance in several generative model. In addition, by extending layer of latent variables to multiple layers, the network is able to learn scale robust features with no task specific regularization or data augmentation. We provide a detailed analysis and show the results of three public benchmarks to confirm the efficiency and reliability of the proposed algorithm.