Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks
Authors: Frank Nussbaum, Jakob Gawlikowski, Julia Niebling
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 frank.nussbaum@uni-jena.de 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 jakob.gawlikowski@dlr.de Julia Niebling German Aerospace Center (DLR) Institute of Data Science Mälzerstraße 3-5, 07745 Jena, Germany julia.niebling@dlr.de |
| 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. |