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 | Conference PDF | Archive PDF | Plain Text | 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. |