Continuous Rotation Group Equivariant Network Inspired by Neural Population Coding
Authors: Zhiqiang Chen, Yang Chen, Xiaolong Zou, Shan Yu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 chenzhiqiang@mails.ucas.ac.cn,yang.chen@ia.ac.cn,xiaolz@mail.pku.edu.cn,shan.yu@nlpr.ia.ac.cn |
| 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. |