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The ability of Variational Autoencoders (VAEs) to learn disentangled representations has made them popular for practical applications.
A generalized solution of the Orthogonal Procrustes problem
Peter H. Schönemann · 1966
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A Unifying Tool for Linear Multivariate Statistical Methods: The RV- Coefficient
P. Robert and Y. Escoufier · 1976
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On Kernel-Target Alignment
Nello Cristianini, John Shawe-Taylor, André Elisseeff, and Jaz S Kandola · 2002
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Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting
Yann LeCun, Fu Jie Huang, and Léon Bottou · 2004
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Measuring Statistical Dependence with Hilbert-Schmidt Norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf · 2005
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Algorithms for Learning Kernels Based on Centered Alignment
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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Matrix computations
Gene H. Golub and Charles F. Van Loan · 2013
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Deep Visual Analogy-Making
Scott Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
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Variational Inference with Normalizing Flows
Danilo Rezende and Shakir Mohamed · 2015
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Generating Sentences from a Continuous Space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio · 2016
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Tutorial on Variational Autoencoders
Carl Doersch · 2016
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Deep Variational Information Bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2017
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β \beta -VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Mohamed Shakir, and Alexander Lerchner · 2017
Cited alongside, same era.
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
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Understanding Disentangling in β \beta -VAE
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Cited alongside, same era.
Isolating Sources of Disentanglement in Variational Autoencoders
Ricky T. Q. Chen, Xuechen Li, Roger B. Grosse, and David K. Duvenaud · 2018
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Diagnosing and Enhancing VAE Models
Bin Dai and David Wipf · 2018
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Universality and Individuality in Neural Dynamics Across Large Populations of Recurrent Networks
Niru Maheswaranathan, Alex Williams, Matthew Golub, Surya Ganguli, and David Sussillo · 2019
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Disentangling Disentanglement in Variational Autoencoders
Emile Mathieu, Tom Rainforth, N Siddharth, and Yee Whye Teh · 2019
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Transfusion: Understanding Transfer Learning for Medical Imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
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Variational Autoencoders Pursue PCA Directions (by Accident)
Michal Rolinek, Dominik Zietlow, and Georg Martius · 2019
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Towards understanding learning representations: To what extent do different neural networks learn the same representation
Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, and John Hopcroft · 2019
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Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2018
Cited alongside, same era.
Insights on Representational Similarity in Neural Networks with Canonical Correlation
Ari Morcos, Maithra Raghu, and Samy Bengio · 2018
Cited alongside, same era.
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig, and Taylor Berg-Kirkpatrick · 2019
Cited alongside, same era.
Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
Cited alongside, same era.
Investigating Multilingual NMT Representations at Scale
Sneha Kudugunta, Ankur Bapna, Isaac Caswell, and Orhan Firat · 2019
Cited alongside, same era.
Quantifying the carbon emissions of machine learning
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres · 2019
Cited alongside, same era.
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Bin Dai, Ziyu Wang, and David Wipf · 2020
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Unsupervised model selection for variational disentangled representation learning
Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Chris Burgess, Alexander Lerchner, and Irina Higgins · 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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What is Being Transferred in Transfer Learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
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Revisiting Model Stitching to Compare Neural Representations
Yamini Bansal, Preetum Nakkiran, and Boaz Barak · 2021
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Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders
Lisa Bonheme and Marek Grzes · 2021
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Grounding Representation Similarity Through Statistical Testing
Frances Ding, Jean-Stanislas Denain, and Jacob Steinhardt · 2021
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Variational autoencoder for reference based image super-resolution
Zhi-Song Liu, Wan-Chi Siu, and Li-Wen Wang · 2021
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Demystifying Inductive Biases for (Beta-) VAE Based Architectures
Dominik Zietlow, Michal Rolinek, and Georg Martius · 2021
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