A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder
[article]
2020
arXiv
pre-print
Preserved Fulltext
Other Versions
We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto-encoder framework. ...
While SeGMA preserves all properties of its semi-supervised predecessors and achieves at least as good generative performance on standard benchmark data sets, it presents additional features: (a) interpolation ...
Our model, which we will call semi-supervised Gaussian mixture auto-encoder (SeGMA), is an adaptation of the Cramer-Wold auto-encoder (CWAE) [13] , which is an instance of WAE models with maximum mean ...
arXiv:1906.09333v2
fatcat:crk7hs6i2fgb3h4v7os6yc2xue
Variational Information Bottleneck for Semi-Supervised Classification
2020
Entropy
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We propose a new formulation of semi-supervised IB with hand crafted and learnable priors and link it to the previous methods such as semi-supervised versions of VAE (M1 + M2), AAE, CatGAN, etc. ...
In this paper, we consider an information bottleneck (IB) framework for semi-supervised classification with several families of priors on latent space representation. ...
The latent space of SeGMA is assumed to follow a mixture of Gaussians. ...
doi:10.3390/e22090943
pmid:33286710
pmcid:PMC7597214
fatcat:chlv5b45qreknpghsaguyt2dce
Cramer-Wold Auto-Encoder
2020
Journal of machine learning research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Inspired by prior works on the Sliced-Wasserstein Auto-Encoders (SWAE) and the Wasserstein Auto-Encoders with MMD-based penalty (WAE-MMD), we propose a new generative model -a Cramer-Wold Auto-Encoder ...
Its main distinguishing feature is that it has a closed-form of the kernel product of radial Gaussians. ...
Śmieja et al. (2019) present a semi-supervised generative model SeGMA, which is able to learn a joint probability distribution of data and their classes. ...
dblp:journals/jmlr/KnopSTPMJ20
fatcat:mwhr3k6jlbfdribuxrvl7wfi6u
2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32
2021
IEEE Transactions on Neural Networks and Learning Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
., +, TNNLS May 2021 2209-2223 SeGMA: Semi-Supervised Gaussian Mixture Autoencoder. ...
Tolooshams, B., +, TNNLS June 2021 2415-2429 SeGMA: Semi-Supervised Gaussian Mixture Autoencoder. ...
doi:10.1109/tnnls.2021.3134132
fatcat:2e7comcq2fhrziselptjubwjme