An Identifiable Double VAE For Disentangled Representations
Authors: Graziano Mita, Maurizio Filippone, Pietro Michiardi
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate superior performance with respect to state-of-the-art approaches, according to several established metrics proposed in the literature on disentanglement. |
| Researcher Affiliation | Collaboration | 1EURECOM, 06410 Biot (France) 2SAP Labs France, 06250 Mougins (France). |
| Pseudocode | No | The paper describes the algorithms and models in text and mathematical formulas but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that 'All methods have been implemented in Py Torch (Paszke et al., 2019)' and that 'The implementation of the metrics is based on Locatello et al. (2019)', but it does not provide an explicit statement or link for the source code of their proposed method (IDVAE). |
| Open Datasets | Yes | We consider four common datasets in the disentanglement literature, where observations are images built as a deterministic function of known generative factors: DSPRITES (Higgins et al., 2017), SHAPES3D (Kim & Mnih, 2018), CARS3D (Reed et al., 2015) and SMALLNORB (Le Cun et al., 2004). |
| Dataset Splits | No | The paper does not provide specific numerical train/validation/test splits (e.g., percentages or counts) or explicitly reference predefined splits that include validation data. It mentions '300 000 training iterations' and evaluation 'At testing time', implying training and test sets, but no explicit validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | All methods have been implemented in Py Torch (Paszke et al., 2019). |
| Experiment Setup | Yes | We tried six different values of regularization strength associated to the target regularization term of each method β for β-VAE, IVAE and IDVAE, and γ for FULLVAE: [1, 2, 4, 6, 8, 16]. For each model configuration and dataset, we run the training procedure with 10 random seeds, given that all methods are susceptible to initialization values. After 300 000 training iterations, every model is evaluated according to the disentanglement metrics described above. The latent dimension z is fixed to the true number of ground-truth factors. |