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..
Learning disentangled representations via product manifold projection
Authors: Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodola
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We widely test our approach on synthetic datasets and more challenging real-world scenarios, outperforming the state of the art in several cases. |
| Researcher Affiliation | Academia | 1Sapienza, University of Rome, Rome, Italy 2Universit a della Svizzera italiana, Lugano, Switzerland. |
| Pseudocode | No | The paper describes the model and losses textually and with diagrams, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We adopted 4 widely used synthetic datasets in order to evaluate the effectivness of our method, namely DSprites (Higgins et al., 2017), Shapes3D (Kim & Mnih, 2018), Cars3D (Reed et al., 2015), Small NORB (Le Cun et al., 2004) . For the experiments on FAUST we used a Point Net (Qi et al., 2017) architecture and a simple MLP as a decoder. |
| Dataset Splits | No | The paper discusses training and testing, and evaluation metrics, but does not explicitly provide details about training/validation/test dataset splits (e.g., percentages, sample counts, or specific split files). |
| Hardware Specification | Yes | We performed our experimental evaluation on a machine equipped with an NVIDA RTX 2080ti, within the Pytorch framework. |
| Software Dependencies | No | The paper mentions using the "Pytorch framework" but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | in all our experiments we use a latent space of dimension d = 10, unless otherwise specified, and k = 10 latent subspaces. |