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..
Self-supervised learning of Split Invariant Equivariant representations
Authors: Quentin Garrido, Laurent Najman, Yann Lecun
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate significant performance gains over existing methods on equivariance related tasks from both a qualitative and quantitative point of view. |
| Researcher Affiliation | Collaboration | 1Meta AI FAIR 2Univ Gustave Eiffel, CNRS, LIGM, F-77454 Marne-la-Vall ee, France 3Courant Institute, New York University 4Center for Data Science, New York University. |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data are available at https://github.com/garridoq/SIE. |
| Open Datasets | Yes | Taking inspiration from 3DIdent (Zimmermann et al., 2021; von K ugelgen et al., 2021) we use renderings of 3D objects from the subset of Shape Net Core (Chang et al., 2015) originating from 3d Warehouse (Trimble Inc). This gives us a total 52472 objects spread across 55 classes. |
| Dataset Splits | Yes | We split the dataset into a training and validation part, containing respectively 80% and 20% of the objects. |
| Hardware Specification | Yes | All experiments are done using 4 NVIDIA V100 GPUs and take around 24 hours. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify its version or the versions of any other software libraries or frameworks used. |
| Experiment Setup | Yes | All methods are trained for 2000 epochs using a resnet-18 encoder and MLP projection head. We use a batch size of 1024 with the Adam optimizer with learning rate 10 3, β1 = 0.9 , β2 = 0.999. |