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..
Continuous Rotation Group Equivariant Network Inspired by Neural Population Coding
Authors: Zhiqiang Chen, Yang Chen, Xiaolong Zou, Shan Yu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that 1) our approach achieves very competitive performances on MNIST-rot with at least 75% fewer parameters compared with previous SOTA methods, which is efficient in parameter; 2) Especially with small sample sizes, our approach exhibits more pronounced performance improvements (up to 24%); 3) It also has excellent rotation generalization ability on various datasets such as MNIST, CIFAR, and Image Net with both plain and Res Net architectures. |
| Researcher Affiliation | Academia | Zhiqiang Chen1,2, Yang Chen2, Xiaolong Zou3, Shan Yu2,4* 1Beijing Academy of Artificial Intelligence, Beijing, China 2Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation(CASIA), Beijing, China 3Qiyuan Lab, Beijing, China 4School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS), Beijing, China EMAIL,EMAIL,EMAIL,EMAIL |
| Pseudocode | No | The paper includes mathematical formulations and descriptions of its methods but does not provide a formally labeled pseudocode block or algorithm. |
| Open Source Code | Yes | https://gitee.com/chenzq/aaai 2024 appendix.git |
| Open Datasets | Yes | Firstly, we test the rotation equivariance in common used benchmark MNIST-rot (Larochelle et al. 2007). Secondly, we test the small sample learning ability on MNIST-rot. Finally, we test the rotation generalization ability on MNIST, CIFAR, and Image Net. See more details about datasets on appendix D. |
| Dataset Splits | No | The paper describes the usage of training and test sets and their augmentation, but does not explicitly mention or detail a separate 'validation' dataset split within the provided text. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or cloud computing instances used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | CRGEN-T is a tiny network containing 6 layers with 10 kernels each layer, which is similar to G-CNN (Cohen and Welling 2016). Each kernel has 16 orientation with 3 sparse points (i.e., ml = 3 in Eq. 4), which has trainable locations and orientations. The generated kernels have a spatial size of 5 5. ... For CRGENLN, we set the weight of the equivariant loss λ = 0.1. |