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..
STARS: Spatial-Temporal Active Re-sampling for Label-Efficient Learning from Noisy Annotations
Authors: Dayou Yu, Weishi Shi, Qi Yu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on both synthetic and real-world data clearly demonstrate the effectiveness of the proposed active re-sampling function. |
| Researcher Affiliation | Academia | Dayou Yu1*, Weishi Shi2*, Qi Yu1 1 Rochester Institute of Technology 2University of North Texas EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods and algorithms in prose and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Yu, D.; Shi, W. S.; and Yu, Q. 2023. Appendix: STARS: Spatial-Temporal Active Re-Sampling for Label-Efficient Learning from Noisy Annotations. https://github.com/ritmininglab/STARS.git. |
| Open Datasets | Yes | We select 5 real-world datasets (Dua and Graff 2017; Shi and Yu 2018) from different domains: medical, bioinformatics, image recognition, and automatic systems. ... Dua, D.; and Graff, C. 2017. UCI Machine Learning Repository. Institution: University of California, Irvine, School of Information and Computer Sciences. |
| Dataset Splits | No | The paper mentions using 'annotated dataset' and 'unlabelled dataset' and evaluates performance on real-world datasets but does not explicitly state the specific percentages or counts for training, validation, and test splits used in its experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions 'Scikit-learn' in the bibliography as a general machine learning library but does not provide specific version numbers for any software dependencies or libraries used in its experiments. |
| Experiment Setup | Yes | For STARS, we linearly increase τ from 0.2 to 0.7 and fix γ to 0.2. ... We use an uncertainty based sampling strategy, Bv SB (Joshi, Porikli, and Papanikolopoulos 2009) for AL to sample new data instances. ... The annotation noise α is set to 0.3. |