Discond-VAE: Disentangling Continuous Factors from the Discrete

17 September 2020

Papers citing "Discond-VAE: Disentangling Continuous Factors from the Discrete"

12 / 12 papers shown

Title
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement Minyoung Kim Yuting Wang Pritish Sahu Vladimir Pavlovic CoGe BDL CML DRL 42 26 0 06 Sep 2019
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement Yeonwoo Jeong Hyun Oh Song 32 49 0 23 May 2019
InfoCatVAE: Representation Learning with Categorical Variational Autoencoders Edouard Pineau Marc Lelarge DRL 57 13 0 20 Jun 2018
Learning Disentangled Joint Continuous and Discrete Representations Emilien Dupont DRL 76 242 0 31 Mar 2018
Disentangling by Factorising Hyunjik Kim A. Mnih CoGe OOD 62 1,346 0 16 Feb 2018
Learning Deep Disentangled Embeddings with the F-Statistic Loss Karl Ridgeway Michael C. Mozer FedML DRL CoGe 54 218 0 14 Feb 2018
Neural Discrete Representation Learning Aaron van den Oord Oriol Vinyals Koray Kavukcuoglu BDL SSL OCL 208 4,989 0 02 Nov 2017
Categorical Reparameterization with Gumbel-Softmax Eric Jang S. Gu Ben Poole BDL 281 5,360 0 03 Nov 2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison A. Mnih Yee Whye Teh BDL 157 2,529 0 02 Nov 2016
Importance Weighted Autoencoders Yuri Burda Roger C. Grosse Ruslan Salakhutdinov BDL 250 1,245 0 01 Sep 2015
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 416 16,944 0 20 Dec 2013
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 224 12,422 0 24 Jun 2012