DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
Authors: Cian Eastwood, Andrei Liviu Nicolicioiu, Julius Von Kügelgen, Armin Kekić, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in our experiments ( 6), we use our extended framework to compare different representations on the MPI3D-Real (Gondal et al., 2019) and Cars3D (Reed et al., 2015) datasets, illustrating the practical usefulness of our E score through its strong correlation with downstream performance. |
| Researcher Affiliation | Academia | Cian Eastwood 1,2, Andrei Liviu Nicolicioiu 1, Julius von Kügelgen 1,3, Armin Keki c1, Frederik Träuble1, Andrea Dittadi1,4, and Bernhard Schölkopf 1 1Max Planck Institute for Intelligent Systems, Tübingen, Germany 2School of Informatics, University of Edinburgh 3Department of Engineering, University of Cambridge 4Technical University of Denmark |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Further details can be found in our open-source code: https://github.com/andreinicolicioiu/DCI-ES. |
| Open Datasets | Yes | Data. We perform our analysis of loss-capacity curves on the MPI3D-Real (Gondal et al., 2019) and Cars3D (Reed et al., 2015) datasets. |
| Dataset Splits | Yes | We split the data into training, validation and test sets of size 295k, 16k, and 726k respectively for MPI3D-Real and 12.6k, 1.4k, 3.4k for Cars3d. We use the validation split for hyperparameter selection and report results on the test split. |
| Hardware Specification | No | The paper describes the probes and training process but does not specify any hardware details like GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'SAGE' but does not provide specific version numbers for any software or libraries. |
| Experiment Setup | Yes | We split the data into training, validation and test sets of size 295k, 16k, and 726k respectively for MPI3D-Real and 12.6k, 1.4k, 3.4k for Cars3d. We use the validation split for hyperparameter selection and report results on the test split. We train MLP probes using the Adam (Kingma & Ba, 2015) optimizer for 100 epochs. We use mean-square error and cross-entropy losses for continuous and discrete factors zj, respectively. To compute Ej, we use the baseline losses of E[zj] and a random classifier for continuous and discrete zj, respectively. |