Class-Balanced Loss Based on Effective Number of Samples
Open accessAbstract
With the rapid increase of large-scale, real-world datasets, it becomes critical to address the problem of long-tailed data distribution (i.e., a few classes account for most of the data, while most classes are under-represented). Existing solutions typically adopt class re-balancing strategies such as re-sampling and re-weighting based on the number of observations for each class. In this work, we argue that as the number of samples increases, the additional benefit of a newly added data point will diminish. We introduce a novel theoretical framework to measure data overlap by associating with each sample a small neighboring region rather than a single point. The effective number of samples is defined as the volume of samples and can be calculated by a simple-formula (1-βn)/(1-β), where n is the number of samples and β ∈ [0, 1) is a hyperparameter. We design a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss. Comprehensive experiments are conducted on artificially induced long-tailed CIFAR datasets and large-scale datasets including ImageNet and iNaturalist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve significant performance gains on long-tailed datasets.
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Model weights were calculated using the effective number of samples class re-weighting scheme so that learning was not affected by the lower frequency of LVEF < 40%.
“Model weights were calculated for the loss function such that learning was not affected by the lower frequency of LVEF < 40% compared to the incidence of LVEF ≥ 40% using the effective number of samples class re-weighting scheme33 (Fig. S2).”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [33]
Re-sampling and re-weighting algorithms focus on rebalancing the class distribution by adjusting the sample probability or loss weight for majority and minority samples.
“Re-sampling and re-weighting algorithms focus on rebalancing the class distribution by adjusting the sample probability/loss weight for majority/minority samples.78, 79, 80, 81, 82, 83”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [79]
The classifier is more inclined to identify classes with a sufficient sample size and rich diversity.
“The classifier is more inclined to identify classes with a sufficient sample size and rich diversity [4,5].”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [5]
When the data has a long-tail distribution, it can lead to bias in the classifier.
“When the data has a long-tail distribution, it can lead to bias in the classifier.”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [5]
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“Given the long-tailed nature of some categories, we automatically reweighed the data based on class distributions and optimized the classifier using a sigmoid cross-entropy class-balanced loss (Cui et al., 2019).”
- Authors
- Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S.
- Affiliations
- Cornell University (US)
- Google (United States) (US)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 9260-9269
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963691377
- Cited by
- 3,094
- References
- 51
- Citations by year
- 2026: 550 · 2025: 549 · 2024: 510 · 2023: 513 · 2022: 414 · 2021: 381 · 2020: 146 · 2019: 31
- FWCI
- 78.33
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- class-balanced loss, class imbalance, data overlap, underrepresented classes, CIFAR, ImageNet, iNaturalist
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