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..
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Authors: Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, Lionel M. NI
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on a number of data sets show that the proposed method outperforms existing CSC algorithms with significantly reduced time and space complexities. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Engineering, Hong Kong University of Science and Technology University, Hong Kong 24Paradigm Inc, Beijing, China. 3Department of Computer and Information Science, University of Macau, Macau. |
| Pseudocode | Yes | Algorithm 1 Sample-dependent CSC (SCSC). 1: Initialize W0 2 W, B0 2 B, H0 = 0, G0 = 0; 2: for t = 1, 2, . . . , T do 3: draw xt from {xi}; 4: xt = F(xt); 5: obtain Wt, Zt using ni APG; 6: for r = 1, 2, . . . , R do 7: Yt(:, r) = F(Zt W > t (:, r)); 8: end for 9: update { Ht(:, :, 1), . . . , Ht(:, :, P)} using (14); 10: update { Gt(:, 1), . . . , Gt(:, P)} using (15); 11: update Bt by (13) using ADMM; 12: end for 13: for r = 1, 2, . . . , R do 14: BT (:, r) = C(F 1( BT (:, r))); 15: end for output BT . |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Experiments are performed on a number of data sets (Table 2). Fruit and City are two small image data sets that have been commonly used in the CSC literature (Zeiler et al., 2010; Bristow et al., 2013; Heide et al., 2015; Papyan et al., 2017). ...In some experiments, we will also use two larger data sets, CIFAR10 (Krizhevsky & Hinton, 2009) and Flower (Nilsback & Zisserman, 2008). |
| Dataset Splits | Yes | We use the default training and testing splits provided in (Bristow et al., 2013). ... Table 2. Summary of the image data sets used. size #training #testing Fruit 100 100 10 4 City 100 100 10 4 CIFAR-10 32 32 50,000 10,000 Flower 500 500 2,040 6,149 |
| Hardware Specification | Yes | Because of the small memory footprint of SCSC, we run it on a GTX 1080 Ti GPU in this experiment. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | Following (Heide et al., 2015; Choudhury et al., 2017; Papyan et al., 2017; Wang et al., 2018), we set the filter size M as 11 11, and the regularization parameter β in (1) as 1. |