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..
Log-Polar Space Convolution Layers
Authors: Bing Su, Ji-Rong Wen
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on different tasks and datasets demonstrate the effectiveness of the proposed LPSC. |
| Researcher Affiliation | Academia | Bing Su, Ji-Rong Wen Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence, Renmin University of China Beijing 100872, China EMAIL; EMAIL |
| Pseudocode | No | The paper describes the calculation of LPSC using mathematical equations (Eq. 1, Eq. 3) and descriptive text, but it does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our code is available at https://github.com/Bing Su12/ Log-Polar-Space-Convolution. |
| Open Datasets | Yes | For image classification, we evaluate the behaviors of LPSC integrated with different CNN architectures on three datasets: CIFAR-10, CIFAR-100 [52], and Image Net [53]. |
| Dataset Splits | Yes | Image Net [53] contains 1.28 million training images and 50k validation images from 1000 classes. |
| Hardware Specification | No | The paper mentions 'Due to the limitation of computing resources, we reduced the batch size and learning rate by 4 times,' but does not provide specific hardware details such as GPU models, CPU types, or cloud instance specifications used for the experiments. |
| Software Dependencies | No | We use the Pytorch [54] implementation2 of these architectures as our baseline. |
| Experiment Setup | Yes | To make a fair comparison, all experimental setup and details including the learning rate, batch size, number of filters per layer, hyper-parameters for the optimizer (e.g., γ, momentum, weight decay) remain exactly the same as in the baseline. |