2018/04/12 by Jiangchao Yao, Ivor Tsang, Ivor W. Tsang +4
Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1804.04435
arxiv created 2018/04/12 · openalex publication_date 2018/04/12 · arxiv updated 2018/04/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Learning in the latent variable model is challenging in the presence of the complex data structure or the intractable latent variable. Previous variational autoencoders can be low effective due to the straightforward encoder-decoder structure. In this paper, we propose a variational composite autoencoder to sidestep this issue by amortizing on top of the hierarchical latent variable model. The experimental results confirm the advantages of our model.