Autoencoder

Known as: Autoassociator, Diabolo network, Auto-encoder 
An autoencoder, autoassociator or Diabolo network is an artificial neural network used for unsupervised learning of efficient codings.The aim of an… (More)
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2017
Review
2017
Although semi-supervised variational autoencoder (SemiVAE) works in image classification task, it fails in text classification… (More)
  • figure 1
  • table 2
  • table 1
  • table 3
  • table 5
Is this relevant?
Review
2017
Review
2017
This paper is a survey and an analysis of different ways of using deep learning to generate musical content. We propose a… (More)
Is this relevant?
Highly Cited
2017
Highly Cited
2017
Representation learning seeks to expose certain aspects of observed data in a learned representation that’s amenable to… (More)
  • figure 1
  • figure 2
  • table 1
  • table 5
  • figure 3
Is this relevant?
Highly Cited
2016
Highly Cited
2016
We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation… (More)
  • figure 1
  • figure 3
  • figure 4
  • table 1
  • table 2
Is this relevant?
Highly Cited
2015
Highly Cited
2015
In this paper we propose a new method for regularizing autoencoders by imposing an arbitrary prior on the latent representation… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • table 1
Is this relevant?
Highly Cited
2015
Highly Cited
2015
There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We… (More)
  • figure 1
  • table 2
  • table 1
  • table 3
  • table 5
Is this relevant?
Highly Cited
2014
Highly Cited
2014
The problem of cross-modal retrieval, e.g., using a text query to search for images and vice-versa, is considered in this paper… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 6
  • figure 4
Is this relevant?
Highly Cited
2013
Highly Cited
2013
We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using… (More)
  • figure 1
  • table 4
  • table 1
  • table 5
  • table 6
Is this relevant?
Highly Cited
2010
Highly Cited
2010
We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoderswhich are trained… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2008
Highly Cited
2008
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial… (More)
  • figure 1
  • figure 2
  • table 1
  • figure 3
Is this relevant?