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..
Sketch Recognition with Natural Correction and Editing
Authors: Jie Wu, Changhu Wang, Liqing Zhang, Yong Rui
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the effectiveness of the proposed algorithms. |
| Researcher Affiliation | Collaboration | 1Brain-Like Computing Lab, Shanghai Jiao Tong University, P. R. China 2Microsoft Research, Beijing, P. R. China |
| Pseudocode | No | The paper describes algorithmic steps in narrative text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We also tested the proposed method in a public benchmark dataset HHReco (Hse and Newton 2004).; In this task, we added correction/editing into hand-drawn full diagrams in the public benchmark dataset (Lemaitre et al. 2013) |
| Dataset Splits | Yes | The leave-one-out cross validation was performed. |
| Hardware Specification | Yes | on a PC with an Intel Core i7-2600 CPU. |
| Software Dependencies | No | The paper mentions software components like 'IDM recognizer' but does not specify any version numbers for these or other ancillary software components (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | No | The paper describes the general experimental setup (e.g., tasks, datasets used, cross-validation), but it does not provide specific hyperparameters (like learning rates, batch sizes, number of epochs) or detailed system-level training configurations. |