Self-Supervised Disentanglement by Leveraging Structure in Data
Augmentations

Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations

15 November 2023

Julius von Kügelgen

Diane Bouchacourt

Pascal Vincent

Bernhard Schölkopf

Papers citing "Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations"

9 / 9 papers shown

Title
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Patrik Reizinger Randall Balestriero David Klindt Wieland Brendel 36 0 0 17 Apr 2025
Identifiable Shared Component Analysis of Unpaired Multimodal Mixtures Subash Timilsina Sagar Shrestha Xiao Fu 11 0 0 28 Sep 2024
Disentanglement with Factor Quantized Variational Autoencoders Gulcin Baykal M. Kandemir Gözde B. Ünal CoGe DRL 24 0 0 23 Sep 2024
InfoNCE: Identifying the Gap Between Theory and Practice E. Rusak Patrik Reizinger Attila Juhos Oliver Bringmann Roland S. Zimmermann Wieland Brendel 33 5 0 28 Jun 2024
A Generative Model of Symmetry Transformations J. Allingham Bruno Mlodozeniec Shreyas Padhy Javier Antorán David Krueger Richard E. Turner Eric T. Nalisnick José Miguel Hernández-Lobato GAN 35 3 0 04 Mar 2024
Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann Yash Sharma Steffen Schneider Matthias Bethge Wieland Brendel SSL 236 207 0 17 Feb 2021
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 238 3,359 0 09 Mar 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 171 313 0 07 Feb 2020
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 282 39,170 0 01 Sep 2014