Name
Abstract | ||
|---|---|---|
The focal loss has demonstrated its effectiveness in many real-world applications such as object detection and image classification, but its theoretical understanding has been limited so far. In this paper, we first prove that the focal loss is classification-calibrated, i.e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified. However, we also prove a negative fact that the focal loss is not strictly proper, i.e., the confidence score of the classifier obtained by focal loss minimization does not match the true class-posterior probability. This may cause the trained classifier to give an unreliable confidence score, which can be harmful in critical applications. To mitigate this problem, we prove that there exists a particular closed-form transformation that can recover the true class-posterior probability from the outputs of the focal risk minimizer. Our experiments show that our proposed transformation successfully improves the quality of class-posterior probability estimation and improves the calibration of the trained classifier, while preserving the same prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPR46437.2021.00516 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
DocType | ISSN | Citations |
Conference | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nontawat Charoenphakdee | 1 | 2 | 4.41 |
| Jayakorn Vongkulbhisal | 2 | 0 | 0.34 |
| Nuttapong Chairatanakul | 3 | 0 | 0.34 |
| Masashi Sugiyama | 4 | 3353 | 264.24 |