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..
Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks
Authors: Frank Nussbaum, Jakob Gawlikowski, Julia Niebling
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show on medical-imaging, earth-observation, and traffic-scene data that rotation criteria based on factor-specific flow probabilities consistently yield the best factors for fine-grained sampling. 5 Experiments The purpose of our experiments is to (1) evaluate rotation criteria based on the quality of rotated factors, (2) demonstrate the merits of fine-grained sample control based on reasonably-rotated factors. |
| Researcher Affiliation | Academia | Frank Nussbaum Friedrich-Schiller-University Jena Fürstengraben 1, 07743 Jena, Germany and German Aerospace Center (DLR) Institute of Data Science Mälzerstraße 3-5, 07745 Jena, Germany EMAIL Jakob Gawlikowski Technical University of Munich Arcisstraße 21, 80333 Munich, Germany and German Aerospace Center (DLR) Institute of Data Science Mälzerstraße 3-5, 07745 Jena, Germany EMAIL Julia Niebling German Aerospace Center (DLR) Institute of Data Science Mälzerstraße 3-5, 07745 Jena, Germany EMAIL |
| Pseudocode | No | The paper does not include a figure, block, or section labeled "Pseudocode" or "Algorithm", nor does it present structured steps in a code-like format. |
| Open Source Code | Yes | Additionally, we made the code for the proposed methods and experiments available under https://github.com/Jakob Code/Structuring SSNs. |
| Open Datasets | Yes | First, we use the LIDC data set [1] in its pre-processed version from [28]... Second, we use the multi-spectral Sentinel-2 data from the SEN12MS data set [36]... Third, we use the Cam Vid data set [5]... |
| Dataset Splits | Yes | Additional details and statistics about the data sets (including splits) can be found in the supplement, where we also detail all training procedures. |
| Hardware Specification | Yes | On a single core of an Intel Xeon Platinum 8260, factor-specific and full flow probabilities can be computed in the sub-second range without significant differences w.r.t. the used rotation, see the supplement for details. |
| Software Dependencies | Yes | We used Python 3.7, particularly with the libraries Py Torch 1.11 [32], scikit-learn [33], Num Py [18], and einops [34]. |
| Experiment Setup | Yes | Additional details and statistics about the data sets (including splits) can be found in the supplement, where we also detail all training procedures. |