1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2020★ 1 cited
Variational Mutual Information Maximization Framework for VAE Latent Codes with Continuous and Discrete Priors
Andriy Serdega, Dae-Shik Kim
Learning interpretable and disentangled representations of data is a key topic in machine learning research. Variational Autoencoder (VAE) is a scalable method for learning directe…
cs.LG2020★ 1 cited
VMI-VAE: Variational Mutual Information Maximization Framework for VAE With Discrete and Continuous Priors
Andriy Serdega, Dae-Shik Kim
Variational Autoencoder is a scalable method for learning latent variable models of complex data. It employs a clear objective that can be easily optimized. However, it does not ex…