Fast Decomposable Submodular Function Minimization using Constrained Total Variation
Authors: Senanayak Sesh Kumar Karri, Francis Bach, Thomas Pock
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our claims by showing results on graph cuts for 2D and 3D graphs. In this section, we consider the minimization of cut functions [3] that are an important examples of submodular functions. In our experiments, we consider the problem of minimizing cuts on 2D images and 3D volumetric surfaces for segmentation. |
| Researcher Affiliation | Academia | K S Sesh Kumar Data Science Institute Imperial College London, UK s.karri@imperial.ac.uk Francis Bach INRIA and Ecole normale superieure PSL Research University, Paris France. francis.bach@inria.fr Thomas Pock Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria. pock@icg.tugraz.at |
| Pseudocode | Yes | Algorithm 1 From SFMDi to SFMC and Algorithm 2 From SFMDi to SFMCi |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions "2D images" and "3D volumetric surfaces" for segmentation but does not provide concrete access information (link, DOI, specific citation to a public dataset) for these datasets. It only describes their size and type. |
| Dataset Splits | No | The paper focuses on an optimization problem (submodular function minimization for graph cuts) rather than a typical machine learning task involving training and validation sets for model generalization. Therefore, it does not provide specific dataset split information (percentages, sample counts, etc.) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | In our approach, we have a parameter ε dependent on √ /n tα , where is the notion of diameter of the base polytope, n is the number of elements in the ground set and t is the number of iterations. In our experiments, we choose ε proportional to √ , √ /t and √ / √ t and respectively represent them by the same terms in Figure 1. |