Variational Composite Autoencoders
Shanghai Jiao Tong University（上海交通大学）；University of Technology Sydney（悉尼科技大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
