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Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent space, making them a staple of representation learning methods.
Learning internal representations by error propagation
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Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Riemannian manifold learning
Tong Lin and Hongbin Zha · 2008
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Contractive auto-encoders: Explicit invariance during feature extraction
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Representation learning: A review and new perspectives, 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Representation learning: A review and new perspectives
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Stochastic backpropagation and approximate inference in deep generative models
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Variational autoencoder based anomaly detection using reconstruction probability
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
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Tom White · 2016
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Elbo surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
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Latent space oddity: on the curvature of deep generative models
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg · 2017
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Deep learning applications in medical image analysis
Justin Ker, Lipo Wang, Jai Rao, and Tchoyoson Lim · 2017
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InfoVAE: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2018
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Geodesic clustering in deep generative models
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher KI Williams · 2018
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Vae with a vampprior
Jakub Tomczak and Max Welling · 2018
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Danilo Jimenez Rezende and Fabio Viola · 2018
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Memorization in overparameterized autoencoders
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3d shapes dataset, 2018
Chris Burgess and Hyunjik Kim · 2018
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Understanding disentangling in β \beta -VAE
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Hyperspherical variational auto-encoders
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Curl: Contrastive unsupervised representations for reinforcement learning
Aravind Srinivas, Michael Laskin, and Pieter Abbeel · 2020
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Towards visually explaining variational autoencoders
Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J Radke, and Octavia Camps · 2020
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Geometry-aware hamiltonian variational auto-encoder
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Learning flat latent manifolds with vaes
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Evaluating representations by the complexity of learning low-loss predictors
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Explorations in homeomorphic variational auto-encoding
Luca Falorsi, Pim de Haan, Tim R Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré, and Taco S Cohen · 2018
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Generating diverse high-fidelity images with vq-vae-2
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Practical lossless compression with latent variables using bits back coding
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An introduction to variational autoencoders
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Diffusion variational autoencoders
Luis A Pérez Rey, Vlado Menkovski, and Jacobus W Portegies · 2019
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On the fairness of disentangled representations
Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem · 2019
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Structure by architecture: Disentangled representations without regularization
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Autoencoding variational autoencoder
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Independent subspace analysis for unsupervised learning of disentangled representations
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Implicit neural representations with periodic activation functions
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Variational autoencoders with riemannian brownian motion priors
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Metrics for probabilistic geometries
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Spatial dependency networks: Neural layers for improved generative image modeling
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Visual representation learning does not generalize strongly within the same domain
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