A General Framework for Robust G-Invariance in G-Equivariant Networks
Authors: Sophia Sanborn, Nina Miolane
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate improved scores in classification accuracy in traditional benchmark datasets as well as improved adversarial robustness. We examine the performance of the G-TC over Max G-Pooling in G-Equivariant Networks defined on these groups and trained on G-Invariant classification tasks. |
| Researcher Affiliation | Academia | Sophia Sanborn sanborn@ucsb.edu Nina Miolane ninamiolane@ucsb.edu Department of Electrical and Computer Engineering UC Santa Barbara |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is publicly available at https://github.com/sophiaas/gtc-invariance. |
| Open Datasets | Yes | For the groups SO(2) and O(2) acting on R2, we use the MNIST dataset of handwritten characters [37], and for the groups SO(3) and O(3) acting on R3, we use the voxelized Model Net10 database of 3D objects [52]. |
| Dataset Splits | Yes | A random 20% of the training dataset is set aside for model validation and is used to tune hyperparameters. The remaining 80% is used for training. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments. |
| Software Dependencies | No | The paper mentions building upon the ESCNN library, but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | Full training details including hyperparameters are provided in Appendix G. All models are trained with a cross-entropy loss, using the Adam optimizer, a learning rate of 0.00005, weight decay of 0.00001, betas of [0.9, 0.999], epsilon of 10-8, a reduce-on-plateau learning rate scheduler with a factor of 0.5, patience of 2 epochs, and a minimum learning rate of 0.0.0001. Each model is trained with four random seeds [0, 1, 2, 3], and results are averaged across seeds. |