Optimizing Rank-based Metrics with Blackbox Differentiation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909425766236160 |
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| author | Rolínek, Michal Musil, Vít Paulus, Anselm Vlastelica, Marin Michaelis, Claudio Martius, Georg |
| author_facet | Rolínek, Michal Musil, Vít Paulus, Anselm Vlastelica, Marin Michaelis, Claudio Martius, Georg |
| contents | Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors. The code is available at https://github.com/martius-lab/blackbox-backprop |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1912_03500 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Optimizing Rank-based Metrics with Blackbox Differentiation Rolínek, Michal Musil, Vít Paulus, Anselm Vlastelica, Marin Michaelis, Claudio Martius, Georg Machine Learning Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors. The code is available at https://github.com/martius-lab/blackbox-backprop |
| title | Optimizing Rank-based Metrics with Blackbox Differentiation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1912.03500 |