PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs
Authors: Zhengyang Shen, Tiancheng Shen, Zhouchen Lin, Jinwen Ma9585-9593
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Experiments, PDO-e S2CNNs show greater parameter efficiency and outperform other spherical CNNs significantly on several tasks. |
| Researcher Affiliation | Academia | 1 School of Mathematical Sciences and LMAM, Peking University 2 Center for Data Science, Peking University 3 The Chinese University of Hong Kong 4 Key Lab. of Machine Perception (Mo E), School of EECS, Peking University 5 Pazhou Lab, Guangzhou, China |
| Pseudocode | No | The paper describes the method mathematically and textually but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not contain an explicit statement offering the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Spherical MNIST Classification We follow (Cohen et al. 2018) in the preparation of the spherical MNIST... and We evaluate our method on the Stanford 2D-3D-S dataset (Armeni et al. 2017)... and Finally, we apply our method to the QM7 dataset (Blum and Reymond 2009; Rupp et al. 2012)... |
| Dataset Splits | Yes | We randomly select 6,000 training images as a validation set, and choose the model with the lowest validation error during training. and We use the official 3-fold cross validation to train and evaluate our model and We use the official 5-fold cross validation to train and evaluate our model |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | No | The data preprocessing, model architectures and training details for each task are provided in the Supplementary Material for reproducing our results. |