Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On Disentangled Representations Learned from Correlated Data
Authors: Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To this end, we report a large-scale empirical study to systematically assess the effect of induced correlations between pairs of factors of variation in training data on the learned representations. We present the first large-scale empirical study (4260 models)1 that examines how modern disentanglement learners perform when ground truth factors of the observational data are correlated. |
| Researcher Affiliation | Collaboration | 1Max Planck Institute for Intelligent Systems, Tübingen, Germany 2University of Toronto and Vector Institute 3Helmholtz AI, Munich 4Amazon (work partly done when FL was at ETH Zurich and MPI-IS) 5Technical University of Denmark 6Mila and Université de Montréal 7CIFAR Azrieli Global Scholar. |
| Pseudocode | No | The paper discusses various algorithms and methods (e.g., VAEs, Ada-GVAE) but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for reproducing experiments is available under https: //github.com/ftraeuble/disentanglement_lib |
| Open Datasets | Yes | Most popular datasets in the disentanglement literature exhibit perfect independence in their Fo V such as d Sprites (Higgins et al., 2017a), Cars3D (Reed et al., 2015), Small NORB (Le Cun et al., 2004), Shapes3D (Kim & Mnih, 2018) or MPI3D variants (Gondal et al., 2019). |
| Dataset Splits | No | The paper mentions 'training data' and 'test data' (referring to OOD data) but does not specify an explicit 'validation' dataset split or its proportion/methodology. |
| Hardware Specification | Yes | Each model was trained for 300,000 iterations on Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions various models and frameworks such as 'variational autoencoders (VAEs)', 'β-VAE', 'Factor VAE', 'Annealed VAE', 'DIP-VAEI', 'DIP-VAE-II' and 'β-TC-VAE', but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Each model was trained for 300,000 iterations on Tesla V100 GPUs. ...each with 6 hyperparameter settings and 5 random seeds. |