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Challenging Common Assumptions in the Unsupervised Learning of
  Disentangled Representations

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

29 November 2018
Francesco Locatello
Stefan Bauer
Mario Lucic
Gunnar Rätsch
Sylvain Gelly
Bernhard Schölkopf
Olivier Bachem
    OOD
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Papers citing "Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations"

50 / 330 papers shown
Title
Towards Learning Controllable Representations of Physical Systems
Towards Learning Controllable Representations of Physical Systems
Kevin Haninger
R. Vicente-Garcia
J. Krüger
31
1
0
16 Nov 2020
Learning to Infer Semantic Parameters for 3D Shape Editing
Learning to Infer Semantic Parameters for 3D Shape Editing
Fangyin Wei
Elena Sizikova
Avneesh Sud
Szymon Rusinkiewicz
Thomas Funkhouser
3DH
3DPC
36
13
0
09 Nov 2020
Learning Causal Semantic Representation for Out-of-Distribution
  Prediction
Learning Causal Semantic Representation for Out-of-Distribution Prediction
Chang-Shu Liu
Xinwei Sun
Jindong Wang
Haoyue Tang
Tao Li
Tao Qin
Wei Chen
Tie-Yan Liu
CML
OODD
OOD
35
104
0
03 Nov 2020
Improving seasonal forecast using probabilistic deep learning
Improving seasonal forecast using probabilistic deep learning
B. Pan
G. Anderson
André Goncalves
Donald D. Lucas
C. Bonfils
Jiwoo Lee
BDL
AI4Cl
11
32
0
27 Oct 2020
On the Transfer of Disentangled Representations in Realistic Settings
On the Transfer of Disentangled Representations in Realistic Settings
Andrea Dittadi
Frederik Trauble
Francesco Locatello
M. Wuthrich
Vaibhav Agrawal
Ole Winther
Stefan Bauer
Bernhard Schölkopf
OOD
35
39
0
27 Oct 2020
Generative Neurosymbolic Machines
Generative Neurosymbolic Machines
Jindong Jiang
Sungjin Ahn
BDL
OCL
225
68
0
23 Oct 2020
Product Manifold Learning
Product Manifold Learning
Sharon Zhang
Amit Moscovich
A. Singer
39
14
0
19 Oct 2020
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for
  Causal Representation Learning
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Sumedh Anand Sontakke
Arash Mehrjou
Laurent Itti
Bernhard Schölkopf
CML
17
60
0
07 Oct 2020
Deep Anomaly Detection by Residual Adaptation
Deep Anomaly Detection by Residual Adaptation
Lucas Deecke
Lukas Ruff
Robert A. Vandermeulen
Hakan Bilen
UQCV
28
4
0
05 Oct 2020
EigenGame: PCA as a Nash Equilibrium
EigenGame: PCA as a Nash Equilibrium
I. Gemp
Brian McWilliams
Claire Vernade
T. Graepel
24
46
0
01 Oct 2020
Multilinear Latent Conditioning for Generating Unseen Attribute
  Combinations
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Markos Georgopoulos
Grigorios G. Chrysos
M. Pantic
Yannis Panagakis
GAN
DRL
19
19
0
09 Sep 2020
Measuring the Biases and Effectiveness of Content-Style Disentanglement
Measuring the Biases and Effectiveness of Content-Style Disentanglement
Xiao Liu
Spyridon Thermos
Gabriele Valvano
A. Chartsias
Alison Q. OÑeil
Sotirios A. Tsaftaris
CoGe
DRL
32
18
0
27 Aug 2020
Linear Disentangled Representations and Unsupervised Action Estimation
Linear Disentangled Representations and Unsupervised Action Estimation
Matthew Painter
Jonathon S. Hare
Adam Prugel-Bennett
CoGe
DRL
39
20
0
18 Aug 2020
Learning Disentangled Expression Representations from Facial Images
Learning Disentangled Expression Representations from Facial Images
Marah Halawa
Manuel Wöllhaf
Eduardo Vellasques
Urko Sánchez Sanz
Olaf Hellwich
CoGe
CVBM
DRL
18
8
0
16 Aug 2020
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric
  Embedding
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
Akira Nakagawa
Keizo Kato
Taiji Suzuki
DRL
19
9
0
30 Jul 2020
A Commentary on the Unsupervised Learning of Disentangled
  Representations
A Commentary on the Unsupervised Learning of Disentangled Representations
Francesco Locatello
Stefan Bauer
Mario Lucic
Gunnar Rätsch
Sylvain Gelly
Bernhard Schölkopf
Olivier Bachem
OOD
DRL
29
20
0
28 Jul 2020
Data-efficient visuomotor policy training using reinforcement learning
  and generative models
Data-efficient visuomotor policy training using reinforcement learning and generative models
Ali Ghadirzadeh
Petra Poklukar
Ville Kyrki
Danica Kragic
Mårten Björkman
OffRL
39
9
0
26 Jul 2020
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part
  Disentanglement
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement
Rinon Gal
Amit H. Bermano
Hao Zhang
Daniel Cohen-Or
3DPC
16
18
0
25 Jul 2020
Learning Disentangled Representations with Latent Variation
  Predictability
Learning Disentangled Representations with Latent Variation Predictability
Xinqi Zhu
Chang Xu
Dacheng Tao
CoGe
DRL
22
26
0
25 Jul 2020
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse
  Coding
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
David A. Klindt
Lukas Schott
Yash Sharma
Ivan Ustyuzhaninov
Wieland Brendel
Matthias Bethge
Dylan M. Paiton
CML
48
132
0
21 Jul 2020
Slot Contrastive Networks: A Contrastive Approach for Representing
  Objects
Slot Contrastive Networks: A Contrastive Approach for Representing Objects
Evan Racah
Sarath Chandar
OCL
DRL
21
14
0
18 Jul 2020
Training Interpretable Convolutional Neural Networks by Differentiating
  Class-specific Filters
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Haoyun Liang
Zhihao Ouyang
Yuyuan Zeng
Hang Su
Zihao He
Shutao Xia
Jun Zhu
Bo Zhang
16
47
0
16 Jul 2020
Towards a Theoretical Understanding of the Robustness of Variational
  Autoencoders
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
A. Camuto
M. Willetts
Stephen J. Roberts
Chris Holmes
Tom Rainforth
AAML
DRL
29
30
0
14 Jul 2020
Failure Modes of Variational Autoencoders and Their Effects on
  Downstream Tasks
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
Yaniv Yacoby
Weiwei Pan
Finale Doshi-Velez
CML
DRL
27
25
0
14 Jul 2020
Counterfactual Data Augmentation using Locally Factored Dynamics
Counterfactual Data Augmentation using Locally Factored Dynamics
Silviu Pitis
Elliot Creager
Animesh Garg
BDL
OffRL
21
85
0
06 Jul 2020
A causal view of compositional zero-shot recognition
A causal view of compositional zero-shot recognition
Y. Atzmon
Felix Kreuk
Uri Shalit
Gal Chechik
OCL
BDL
CML
61
118
0
25 Jun 2020
Capturing Label Characteristics in VAEs
Capturing Label Characteristics in VAEs
Thomas Joy
Sebastian M. Schmon
Philip Torr
N. Siddharth
Tom Rainforth
CML
DRL
30
43
0
17 Jun 2020
Learning from Demonstration with Weakly Supervised Disentanglement
Learning from Demonstration with Weakly Supervised Disentanglement
Yordan V. Hristov
S. Ramamoorthy
DRL
21
9
0
16 Jun 2020
On Disentangled Representations Learned From Correlated Data
On Disentangled Representations Learned From Correlated Data
Frederik Trauble
Elliot Creager
Niki Kilbertus
Francesco Locatello
Andrea Dittadi
Anirudh Goyal
Bernhard Schölkopf
Stefan Bauer
OOD
CML
29
115
0
14 Jun 2020
Convolutional Generation of Textured 3D Meshes
Convolutional Generation of Textured 3D Meshes
Dario Pavllo
Graham Spinks
Thomas Hofmann
Marie-Francine Moens
Aurelien Lucchi
19
62
0
13 Jun 2020
What Matters In On-Policy Reinforcement Learning? A Large-Scale
  Empirical Study
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Marcin Andrychowicz
Anton Raichuk
Piotr Stańczyk
Manu Orsini
Sertan Girgin
...
M. Geist
Olivier Pietquin
Marcin Michalski
Sylvain Gelly
Olivier Bachem
OffRL
31
213
0
10 Jun 2020
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Xiaojie Guo
Liang Zhao
Zhao Qin
Lingfei Wu
Amarda Shehu
Yanfang Ye
CoGe
DRL
38
46
0
09 Jun 2020
High-Fidelity Audio Generation and Representation Learning with Guided
  Adversarial Autoencoder
High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder
Kazi Nazmul Haque
R. Rana
Björn W Schuller
DRL
26
12
0
01 Jun 2020
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement
  and Data Generation
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
Yizhe Zhu
Martin Renqiang Min
Asim Kadav
H. Graf
CoGe
DRL
27
95
0
23 May 2020
Deep Learning and Knowledge-Based Methods for Computer Aided Molecular
  Design -- Toward a Unified Approach: State-of-the-Art and Future Directions
Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions
Abdulelah S. Alshehri
R. Gani
Fengqi You
AI4CE
30
83
0
18 May 2020
Face Identity Disentanglement via Latent Space Mapping
Face Identity Disentanglement via Latent Space Mapping
Yotam Nitzan
Amit H. Bermano
Yangyan Li
Daniel Cohen-Or
CVBM
CoGe
DRL
30
16
0
15 May 2020
Towards Efficient Processing and Learning with Spikes: New Approaches
  for Multi-Spike Learning
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
Qiang Yu
Shenglan Li
Huajin Tang
Longbiao Wang
J. Dang
Kay Chen Tan
27
10
0
02 May 2020
A Deeper Look at the Unsupervised Learning of Disentangled
  Representations in $β$-VAE from the Perspective of Core Object
  Recognition
A Deeper Look at the Unsupervised Learning of Disentangled Representations in βββ-VAE from the Perspective of Core Object Recognition
Harshvardhan Digvijay Sikka
OCL
OOD
BDL
DRL
21
1
0
25 Apr 2020
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Girish A. Koushik
Furui Liu
Zhitang Chen
Xinwei Shen
Jianye Hao
Jun Wang
OOD
CoGe
CML
41
44
0
18 Apr 2020
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Lisa Lee
Benjamin Eysenbach
Ruslan Salakhutdinov
S. Gu
Chelsea Finn
SSL
17
26
0
06 Apr 2020
A theory of independent mechanisms for extrapolation in generative
  models
A theory of independent mechanisms for extrapolation in generative models
M. Besserve
Rémy Sun
Dominik Janzing
Bernhard Schölkopf
20
25
0
01 Apr 2020
How Useful is Self-Supervised Pretraining for Visual Tasks?
How Useful is Self-Supervised Pretraining for Visual Tasks?
Alejandro Newell
Jia Deng
SSL
25
136
0
31 Mar 2020
Semi-Supervised StyleGAN for Disentanglement Learning
Semi-Supervised StyleGAN for Disentanglement Learning
Weili Nie
Tero Karras
Animesh Garg
Shoubhik Debhath
Anjul Patney
Ankit B. Patel
Anima Anandkumar
DRL
89
72
0
06 Mar 2020
Generalizable semi-supervised learning method to estimate mass from
  sparsely annotated images
Generalizable semi-supervised learning method to estimate mass from sparsely annotated images
Muhammad K. A. Hamdan
D. Rover
Matthew J. Darr
John Just
20
7
0
05 Mar 2020
NestedVAE: Isolating Common Factors via Weak Supervision
NestedVAE: Isolating Common Factors via Weak Supervision
M. Vowels
Necati Cihan Camgöz
Richard Bowden
CML
DRL
26
21
0
26 Feb 2020
Weakly-Supervised Disentanglement Without Compromises
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello
Ben Poole
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
Michael Tschannen
CoGe
OOD
DRL
184
313
0
07 Feb 2020
Fully-hierarchical fine-grained prosody modeling for interpretable
  speech synthesis
Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis
Guangzhi Sun
Yu Zhang
Ron J. Weiss
Yuanbin Cao
Heiga Zen
Yonghui Wu
11
130
0
06 Feb 2020
Evaluating Weakly Supervised Object Localization Methods Right
Evaluating Weakly Supervised Object Localization Methods Right
Junsuk Choe
Seong Joon Oh
Seungho Lee
Sanghyuk Chun
Zeynep Akata
Hyunjung Shim
WSOL
300
186
0
21 Jan 2020
Deep Automodulators
Deep Automodulators
Ari Heljakka
Wenshuai Zhao
Arno Solin
Arno Solin
31
5
0
21 Dec 2019
Triple Generative Adversarial Networks
Triple Generative Adversarial Networks
Chongxuan Li
Kun Xu
Jiashuo Liu
Jun Zhu
Bo Zhang
GAN
31
41
0
20 Dec 2019
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