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..
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Authors: Heinrich Jiang, Jennifer Jang, Samory Kpotufe
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then show strong clustering performance on real datasets as well as promising applications to image segmentation.Despite the simplicity of our approach, we show that Quickshift++ considerably outperforms the popular density-based clustering algorithms, while being efficient.5. Simulations, 6. Image Segmentation, 7. Clustering Experiments |
| Researcher Affiliation | Collaboration | 1Google Research, Mountain View, CA 2Uber Inc, San Francisco, CA 3Princeton University, Princeton, NJ. |
| Pseudocode | Yes | Algorithm 1 MCores (estimating cluster-cores) and Algorithm 2 Quickshift++ |
| Open Source Code | Yes | Code release is at https://github.com/google/quickshift. |
| Open Datasets | Yes | Datasets Used: Summary of the datasets can be found in Figure 8. Seeds, glass, and iris are standard UCI datasets (Lichman, 2013) used for clustering... We also used a small subset of MNIST (Le Cun et al., 2010) for our experiments. |
| Dataset Splits | No | The paper runs experiments on various real datasets and scores against groundtruth, but it does not explicitly provide details about train/validation/test splits, percentages, or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or other system specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions general software like 'scikit-learn' but does not provide specific version numbers for any key software components or libraries required to replicate the experiments. |
| Experiment Setup | Yes | Quickshift++ settings were fixed at k = 20, β = 0.7 for all the datasets (Figure 3). β = 0.9 is reasonable across a wide range of images, β was fixed to this value for segmentation here. (Section 6). Quick Shift was set with bandwidth 10 and Quickshift++ was set with k = 300 and β = 0.9. (Figure 4 caption). β = 0.3 for all but one of the datasets (Section 7). The procedures were tuned in their respective essential hyperparameter: k-means number of clusters, DBSCAN epsilon, MCores k, mean shift bandwidth, quick shift bandwidth, Quickshift++ k. (Figure 9 caption) |