Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2003.11657 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910743567269888 |
|---|---|
| author | Rolínek, Michal Swoboda, Paul Zietlow, Dominik Paulus, Anselm Musil, Vít Martius, Georg |
| author_facet | Rolínek, Michal Swoboda, Paul Zietlow, Dominik Paulus, Anselm Musil, Vít Martius, Georg |
| contents | Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups. The code is available at https://github.com/martius-lab/blackbox-deep-graph-matching |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2003_11657 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers Rolínek, Michal Swoboda, Paul Zietlow, Dominik Paulus, Anselm Musil, Vít Martius, Georg Machine Learning Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups. The code is available at https://github.com/martius-lab/blackbox-deep-graph-matching |
| title | Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2003.11657 |