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In particular, I'll be explaining the technique used in "Semi-supervised Learning with Deep Generative Models" by Kingma et al.

Semi-supervised Learning with Deep Generative Models Diederik P. Kingma , Danilo J. Rezende y, Shakir Mohamed , Max Welling Machine Learning Group, Univ. of Amsterdam,fD.P.Kingma, M.Wellingg@uva.nl

Semi-supervised Learning with. Deep Generative Models. Diederik P. Kingma∗, Danilo J. Rezende†, Shakir Mohamed†, Max Welling∗. ∗Machine Learning Group ...

In this paper, we instead, choose to exploit the power of generative models, which recognise the semi-supervised learning problem as a specialised missing data imputation task for the classification problem.Existing generative approaches based on models such as Gaussian mixture or hidden Markov models (Zhu, 2006), have not been very successful due to the need …

Deep semisupervised learning methods include generative methods, graph-based methods [13], consistency regularized based methods [24] etc. The two deep ...

Notations; Latent feature discriminative model (M1); Generative semi-supervised model (M2); M1+M2; Variational distribution ...

24.01.2019 · We build two new models out of two previous deep generative models proposed for semi-supervised learning. The new models can learn in the unsupervised case as well as in the semi-supervised case. Semi-unsupervised learning has similarities to some varieties of zero-shot learning (ZSL), where deep generative models have been of interest Weiss et al. ( 2016 )

04.09.2017 · This work presents a novel semi-supervised learning approach for data-driven modeling of asset failures when health status is only partially known in historical data. We combine a generative model parameterized by deep neural networks with non-linear embedding technique. It allows us to build prognostic models with the limited amount of health status …

Title:Semi-Supervised Learning with Deep Generative Models ... Abstract: The ever-increasing size of modern data sets combined with the difficulty ...

Dec 08, 2014 · Semi-supervised learning with deep generative models Pages 3581–3589 ABSTRACT References Index Terms Comments ABSTRACT The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.

08.12.2014 · We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far …

We show qualitatively generative semi-supervised models learn to separate the data classes (content types) from the intra-class variabilities (styles), allowing in a very straightforward fashion to simulate analogies of images on a variety of datasets. 2 Deep Generative Models for Semi-supervised Learning

We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide ...

Learning Disentangled Representations with Semi-Supervised Deep Generative Models ... Variational autoencoders (VAEs) learn representations of data by jointly ...

Deep Spotlight @ NIPS'15, Montreal, Canada.Our paper shows how to do semi-supervised learning with ...

Semi-Supervised Learning with Deep Generative Models. NeurIPS 2014 · Diederik P. Kingma , Danilo J. Rezende , Shakir Mohamed , Max Welling ·. Edit social preview. The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant ...

Semi-Supervised Learning with Deep Generative Models. NeurIPS 2014 · Diederik P. Kingma , Danilo J. Rezende , Shakir Mohamed , Max Welling ·. Edit social preview. The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant ...

2 Deep Generative Models for Semi-supervised Learning We are faced with data that appear as pairs (X,Y) = {(x1,y1),…,(xN,yN)}, with the i -th observation xi ∈ RD and the corresponding class label yi ∈ {1,…,L} . Observations will have corresponding latent variables, which we denote by zi .

Jun 27, 2021 · Semi-supervised learning Semi-supervised learning (SSL) deals with the situation where few labeled training examples are available together with a significant number of unlabeled samples. Despite...

Semi-supervised learning with deep generative models · Discussion and Conclusion · The authors have developed an efficient variational optimisation algorithm for ...

Semi-supervised Learning with. Deep Generative Models. Diederik P. Kingma∗, Danilo J. Rezende†, Shakir Mohamed†, Max Welling∗ ∗Machine Learning Group ...