On Causally Disentangled Representations
Authors: Abbavaram Gowtham Reddy, Benin Godfrey L, Vineeth N Balasubramanian8089-8097
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 cs19resch11002@iith.ac.in, benin.godfrey@cse.iith.ac.in, vineethnb@iith.ac.in |
| 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... |