# Class Confidence Aware Reweighting for Long Tailed Learning

arXiv: https://arxiv.org/abs/2601.15924 · HTML: https://mlsanity.com/paper/2601.15924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2007.10740/) (2020)
- [Parametric Contrastive Learning](https://mlsanity.com/paper/2107.12028/) (2021)
- [Cross-Entropy Loss Functions: Theoretical Analysis and Applications](https://mlsanity.com/paper/2304.07288/) (2023)
- [Natural Language Processing Advancements By Deep Learning: A Survey](https://mlsanity.com/paper/2003.01200/) (2020)
- [Long-tailed Recognition with Model Rebalancing](https://mlsanity.com/paper/2510.08177/) (2025)
- [Difficulty-aware Balancing Margin Loss for Long-tailed Recognition](https://mlsanity.com/paper/2412.15477/) (2024)
- [Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2305.10648/) (2023)
- [Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation](https://mlsanity.com/paper/2406.03398/) (2024)
- [Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation](https://mlsanity.com/paper/2203.00452/) (2022)
- [Gradient-Aware Logit Adjustment Loss for Long-tailed Classifier](https://mlsanity.com/paper/2403.09036/) (2024)

## Abstract (arXiv)

Deep neural network models degrade significantly in the long-tailed data distribution, with the overall training data dominated by a small set of classes in the head, and the tail classes obtaining less training examples. Addressing the imbalance in the classes, attention in the related literature was given mainly to the adjustments carried out in the decision space in terms of either corrections performed at the logit level in order to compensate class-prior bias, with the least attention to the optimization process resulting from the adjustments introduced through the differences in the confidences among the samples. In the current study, we present the design of a class and confidence-aware re-weighting scheme for long-tailed learning. This scheme is purely based upon the loss level and has a complementary nature to the existing methods performing the adjustment of the logits. In the practical implementation stage of the proposed scheme, we use an Omega(pt, fc) function. This function enables the modulation of the contribution towards the training task based upon the confidence value of the prediction, as well as the relative frequency of the corresponding class. Our observations in the experiments are corroborated by significant experimental results performed on the CIFAR-100-LT, ImageNet-LT, and iNaturalist2018 datasets under various values of imbalance factors that clearly authenticate the theoretical discussions above.