Self-supervised learning of Split Invariant Equivariant representations
Authors: Quentin Garrido, Laurent Najman, Yann Lecun
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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. |