Neural ePDOs: Spatially Adaptive Equivariant Partial Differential Operator Based Networks
Authors: Lingshen He, Yuxuan Chen, Zhengyang Shen, Yibo Yang, Zhouchen Lin
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we show that our method can significantly improve previous works with smaller model size in various datasets. Especially, we achieve the state-of-the-art performance on the MNIST-rot dataset with only tenth of parameters of the previous best model. |
| Researcher Affiliation | Collaboration | Lingshen He1, Yuxuan Chen2 , Zhengyang Shen3, Yibo Yang2, Zhouchen Lin1,4,5 1 National Key Lab of General AI, School of Intelligence Science and Technology, Peking University 2 JD Explore Academy, Beijing, China 3 Department of Computer Vision Technology (VIS), Baidu Inc. 4 Institute for Artificial Intelligence, Peking University 5 Peng Cheng Laboratory |
| Pseudocode | No | The paper describes the proposed methods and their implementation but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We first test our model on MNIST-rot dataset (Larochelle et al., 2007), which is a standard benchmark to test the equivariant models. The dataset contains 62k 28 28 randomly rotated gray-scale handwritten digits. Image Net (Deng et al., 2009) is a large-scale dataset that consists of 1000 classes with roughly 1000 images per class, which is a common benchmark for image recognition. |
| Dataset Splits | Yes | It contains 1.2 million training images and 50k validation images. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud instance types used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | More details about the model, training and hyperparameters analysis can be found in the supplementary material. More detailed training settings can be found in the supplementary material. |