STARS: Spatial-Temporal Active Re-sampling for Label-Efficient Learning from Noisy Annotations
Authors: Dayou Yu, Weishi Shi, Qi Yu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 {dy2507, qi.yu}@rit.edu, weishi.shi@unt.edu |
| 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. |