The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
A General and Adaptive Robust Loss Function
Open accessAbstract
We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By introducing robustness as a continuous parameter, our loss function allows algorithms built around robust loss minimization to be generalized, which improves performance on basic vision tasks such as registration and clustering. Interpreting our loss as the negative log of a univariate density yields a general probability distribution that includes normal and Cauchy distributions as special cases. This probabilistic interpretation enables the training of neural networks in which the robustness of the loss automatically adapts itself during training, which improves performance on learning-based tasks such as generative image synthesis and unsupervised monocular depth estimation, without requiring any manual parameter tuning.
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Cites
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Cited by
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving
Ravi Kumar, V., Yogamani, S., Rashed, H., Sitsu, G., Witt, C., Leang, I., Milz, S., & Mader, P. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3062324 · Cited by 121
“It is a linear combination of the general robust pixel-wise loss term [12] and the Structural Similarity (SSIM) [13] as described in [14].”
MethodFinger sweat analysis enables short interval metabolic biomonitoring in humans
Brunmair, J., Gotsmy, M., Niederstaetter, L., Neuditschko, B., Bileck, A., Slany, A., Feuerstein, M. L., Langbauer, C., Janker, L., Zanghellini, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26245-4 · Cited by 96
Introspective perception for mobile robots
Rabiee, S., & Biswas, J. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103999 · Cited by 6
Metadata
- Authors
- Barron, J. T.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 4326-4334
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2899248093
- Cited by
- 689
- References
- 37
- FWCI
- 21.08
- Type
- conference paper
- Language
- en
- Open access
- green
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