# Posterior Re-calibration for Imbalanced Datasets

arXiv: https://arxiv.org/abs/2010.11820 · HTML: https://mlsanity.com/paper/2010.11820/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 30 of 53)

- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Self-supervised Learning is More Robust to Dataset Imbalance](https://mlsanity.com/paper/2110.05025/) (2021)
- [Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition](https://mlsanity.com/paper/2107.09249/) (2021)
- [Balanced MSE for Imbalanced Visual Regression](https://mlsanity.com/paper/2203.16427/) (2022)
- [RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression](https://mlsanity.com/paper/2205.15236/) (2022)
- [TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification](https://mlsanity.com/paper/2206.12917/) (2022)
- [On Model Calibration for Long-Tailed Object Detection and Instance Segmentation](https://mlsanity.com/paper/2107.02170/) (2021)
- [Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimation](https://mlsanity.com/paper/2302.01094/) (2023)
- [Long-Tailed Partial Label Learning via Dynamic Rebalancing](https://mlsanity.com/paper/2302.05080/) (2023)
- [Use Your Head: Improving Long-Tail Video Recognition](https://mlsanity.com/paper/2304.01143/) (2023)
- [Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes](https://mlsanity.com/paper/2102.12894/) (2021)
- [Striking the Right Balance: Recall Loss for Semantic Segmentation](https://mlsanity.com/paper/2106.14917/) (2021)
- [ConR: Contrastive Regularizer for Deep Imbalanced Regression](https://mlsanity.com/paper/2309.06651/) (2023)
- [Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data](https://mlsanity.com/paper/2502.10381/) (2025)
- [Semi-Supervised Graph Imbalanced Regression](https://mlsanity.com/paper/2305.12087/) (2023)
- [Latent-based Diffusion Model for Long-tailed Recognition](https://mlsanity.com/paper/2404.04517/) (2024)
- [Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition](https://mlsanity.com/paper/2310.18765/) (2023)
- [LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection](https://mlsanity.com/paper/2503.01900/) (2025)
- [Minimum-Risk Recalibration of Classifiers](https://mlsanity.com/paper/2305.10886/) (2023)
- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail](https://mlsanity.com/paper/2304.00101/) (2023)
- [Improved Balanced Classification with Theoretically Grounded Loss Functions](https://mlsanity.com/paper/2512.23947/) (2025)
- [Taming the Long Tail in Human Mobility Prediction](https://mlsanity.com/paper/2410.14970/) (2024)
- [Training Class-Imbalanced Diffusion Model Via Overlap Optimization](https://mlsanity.com/paper/2402.10821/) (2024)
- [Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning](https://mlsanity.com/paper/2405.19789/) (2024)
- [Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms](https://mlsanity.com/paper/2206.05749/) (2022)
- [Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling](https://mlsanity.com/paper/2208.09130/) (2022)
- [Subspace Prototype Guidance for Mitigating Class Imbalance in Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2408.10537/) (2024)
- [Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout](https://mlsanity.com/paper/2303.06314/) (2023)
- [Class Similarity Transition: Decoupling Class Similarities and Imbalance from Generalized Few-shot Segmentation](https://mlsanity.com/paper/2404.05111/) (2024)

## Abstract (arXiv)

Neural Networks can perform poorly when the training label distribution is heavily imbalanced, as well as when the testing data differs from the training distribution. In order to deal with shift in the testing label distribution, which imbalance causes, we motivate the problem from the perspective of an optimal Bayes classifier and derive a post-training prior rebalancing technique that can be solved through a KL-divergence based optimization. This method allows a flexible post-training hyper-parameter to be efficiently tuned on a validation set and effectively modify the classifier margin to deal with this imbalance. We further combine this method with existing likelihood shift methods, re-interpreting them from the same Bayesian perspective, and demonstrating that our method can deal with both problems in a unified way. The resulting algorithm can be conveniently used on probabilistic classification problems agnostic to underlying architectures. Our results on six different datasets and five different architectures show state of art accuracy, including on large-scale imbalanced datasets such as iNaturalist for classification and Synthia for semantic segmentation. Please see https://github.com/GT-RIPL/UNO-IC.git for implementation.