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 Causally Disentangled Representations
Authors: Abbavaram Gowtham Reddy, Benin Godfrey L, Vineeth N Balasubramanian8089-8097
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform an empirical study on state of the art disentangled representation learners using our metrics and dataset to evaluate them from causal perspective. |
| Researcher Affiliation | Academia | Abbavaram Gowtham Reddy, Benin Godfrey L, Vineeth N Balasubramanian Indian Institute of Technology Hyderabad, India EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The algorithms detailing the implementation of UC and CG metrics are provided in the Appendix. |
| Open Source Code | Yes | CANDLE dataset, code, and the Appendix of this work are made publicly available at https://causal-disentanglement.github.io/IITHCANDLE/. |
| Open Datasets | Yes | We introduce an image dataset called CANDLE (Causal ANalysis in Disentang Led r Epresentations) with 6 data generating factors along with both observed and unobserved confounders... CANDLE dataset, code, and the Appendix of this work are made publicly available at https://causal-disentanglement.github.io/IITHCANDLE/. ... d Sprites (Matthey et al. 2017), MPI3D (Gondal et al. 2019). |
| Dataset Splits | No | The paper mentions “Semi-supervision is provided by using labels for 10% of data points” but does not specify explicit training/validation/test splits (e.g., percentages or counts) for the datasets used in experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions “Blender (Community 2018)” for dataset creation and “open-source disentanglement library (Locatello et al. 2019)” for training models, but it does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | Semi-supervision is provided by using labels for 10% of data points. Additional details on the experimental setup and qualitative results are provided in the Appendix. Our loss function w.r.t. dataset D = {xi}L i=1 is given by: LS S FVAE BB = L(Factor VAE) +λ i=1 ||xi wi ˆxi wi||2 2 (6) where ... λ is a hyperparameter... |