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Measuring Disentanglement: A Review of Metrics

Measuring Disentanglement: A Review of Metrics

16 December 2020
M. Carbonneau
Julian Zaïdi
Jonathan Boilard
G. Gagnon
    CoGe
    DRL
ArXivPDFHTML

Papers citing "Measuring Disentanglement: A Review of Metrics"

13 / 13 papers shown
Title
Disentangling representations of retinal images with generative models
Disentangling representations of retinal images with generative models
Sarah Muller
Lisa M. Koch
Hendrik P. A. Lensch
Philipp Berens
MedIm
32
3
0
29 Feb 2024
Matching aggregate posteriors in the variational autoencoder
Matching aggregate posteriors in the variational autoencoder
Surojit Saha
Sarang Joshi
Ross T. Whitaker
DRL
34
4
0
13 Nov 2023
Disentanglement Learning via Topology
Disentanglement Learning via Topology
Nikita Balabin
Daria Voronkova
I. Trofimov
Evgeny Burnaev
S. Barannikov
DRL
58
2
0
24 Aug 2023
Measuring the Effect of Causal Disentanglement on the Adversarial
  Robustness of Neural Network Models
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models
Preben Ness
D. Marijan
Sunanda Bose
CML
29
0
0
21 Aug 2023
Disentangling Learning Representations with Density Estimation
Disentangling Learning Representations with Density Estimation
Eric C. Yeats
Frank Liu
Hai Helen Li
BDL
DRL
CML
48
2
0
08 Feb 2023
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative
  Models in Engineering Design
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design
Lyle Regenwetter
Akash Srivastava
Dan Gutfreund
Faez Ahmed
24
28
0
06 Feb 2023
Disentangled Representation Learning
Disentangled Representation Learning
Xin Wang
Hong Chen
Siao Tang
Zihao Wu
Wenwu Zhu
DRL
35
78
0
21 Nov 2022
Music Mixing Style Transfer: A Contrastive Learning Approach to
  Disentangle Audio Effects
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects
Junghyun Koo
Marco A. Martínez-Ramírez
Wei-Hsiang Liao
Stefan Uhlich
Kyogu Lee
Yuki Mitsufuji
45
18
0
04 Nov 2022
DOT-VAE: Disentangling One Factor at a Time
DOT-VAE: Disentangling One Factor at a Time
Vaishnavi Patil
Matthew Evanusa
J. JáJá
CoGe
DRL
CML
23
1
0
19 Oct 2022
Gromov-Wasserstein Autoencoders
Gromov-Wasserstein Autoencoders
Nao Nakagawa
Ren Togo
Takahiro Ogawa
Miki Haseyama
GAN
DRL
26
11
0
15 Sep 2022
Disentangling representations in Restricted Boltzmann Machines without
  adversaries
Disentangling representations in Restricted Boltzmann Machines without adversaries
Jorge Fernandez-de-Cossio-Diaz
Simona Cocco
R. Monasson
DRL
40
13
0
23 Jun 2022
GlanceNets: Interpretabile, Leak-proof Concept-based Models
GlanceNets: Interpretabile, Leak-proof Concept-based Models
Emanuele Marconato
Andrea Passerini
Stefano Teso
106
64
0
31 May 2022
Self-Supervised Learning Disentangled Group Representation as Feature
Self-Supervised Learning Disentangled Group Representation as Feature
Tan Wang
Zhongqi Yue
Jianqiang Huang
Qianru Sun
Hanwang Zhang
OOD
36
67
0
28 Oct 2021
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