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Class-Balanced Softmax Improves Imbalanced Data Recognition

Yi-Hang Zhu, Rajeev Raman, Shiqi Su, Jianyuan Sun, Xinyu Yang, Nan Xing, Huiyu Zhou· July 27, 2026 View original

Summary

Researchers propose Class-Balanced Softmax (CBS), a new logit adjustment method rooted in Bayesian theory, to address the performance degradation of deep learning models on imbalanced datasets. CBS effectively mitigates the "preference issue" where models perform poorly on tail classes, outperforming existing methods like Balanced Softmax.

Deep learning models often struggle with imbalanced datasets, where certain classes have significantly fewer examples than others, leading to poor performance on these "tail" classes. While methods like Balanced Softmax exist, they still have limitations, particularly in achieving high accuracy for these underrepresented categories. A new approach, Class-Balanced Softmax (CBS), offers a solution. It's a computationally inexpensive logit adjustment method based on a Bayesian framework and a power-law assumption, making it easy to integrate into existing deep learning pipelines. CBS specifically targets and mitigates the "preference issue," a phenomenon where models trained on imbalanced data exhibit higher training error and a larger generalization gap for data-limited classes. Extensive experiments on large-scale benchmarks demonstrate that CBS is highly scalable and consistently outperforms current state-of-the-art methods, including Balanced Softmax, by effectively improving recognition for long-tailed distributions.

Why it matters

Data scientists and AI engineers can significantly improve the accuracy and fairness of their classification models when working with real-world, often imbalanced, datasets, leading to more robust and reliable AI systems.

How to implement this in your domain

  1. 1Identify existing classification models that perform poorly on imbalanced datasets or specific tail classes.
  2. 2Implement Class-Balanced Softmax as a logit adjustment in a pilot project.
  3. 3Compare the performance of CBS against current rebalancing methods like Balanced Softmax using relevant metrics.
  4. 4Integrate CBS into standard model training pipelines for applications dealing with long-tailed data distributions.
  5. 5Educate data science teams on the benefits and implementation of advanced rebalancing techniques.

Who benefits

HealthcareFinanceE-commerceManufacturingSecurity

Key takeaways

  • Class-Balanced Softmax (CBS) improves deep learning performance on imbalanced datasets.
  • It's a simple, computationally inexpensive logit adjustment based on Bayesian theory.
  • CBS effectively mitigates the "preference issue" for tail classes.
  • The method outperforms existing rebalancing techniques like Balanced Softmax.

Original post by Yi-Hang Zhu, Rajeev Raman, Shiqi Su, Jianyuan Sun, Xinyu Yang, Nan Xing, Huiyu Zhou

"arXiv:2607.22258v1 Announce Type: new Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks. However, their performance degrades significantly on imbalanced datasets. Although Balanced Softmax is wide…"

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Originally posted by Yi-Hang Zhu, Rajeev Raman, Shiqi Su, Jianyuan Sun, Xinyu Yang, Nan Xing, Huiyu Zhou on X · view source

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