2021/06/02 by Keisuke Kawano, Kawano, Keisuke, Satoshi Koide +3
Computer Science · Engineering · Mathematics · #Advanced Neural Network Applications #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2106.00886
openalex publication_date 2021/06/02 · arxiv created 2021/12/08 · arxiv updated 2021/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider a general task called partial Wasserstein covering with the goal of providing information on what patterns are not being taken into account in a dataset (e.g., dataset used during development) compared with another dataset(e.g., dataset obtained from actual applications). We model this task as a discrete optimization problem with partial Wasserstein divergence as an objective function. Although this problem is NP-hard, we prove that it satisfies the submodular property, allowing us to use a greedy algorithm with a 0.63 approximation. However, the greedy algorithm is still inefficient because it requires solving linear programming for each objective function evaluation. To overcome this inefficiency, we propose quasi-greedy algorithms that consist of a series of acceleration techniques, such as sensitivity analysis based on strong duality and the so-called C-transform in the optimal transport field. Experimentally, we demonstrate that we can efficiently fill in the gaps between the two datasets and find missing scene in real driving scenes datasets.