# Imbalanced Semi-supervised Learning with Bias Adaptive Classifier

arXiv: https://arxiv.org/abs/2207.13856 · HTML: https://mlsanity.com/paper/2207.13856/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 67 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [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)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning](https://mlsanity.com/paper/1606.04586/) (2016)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Equalization Loss for Long-Tailed Object Recognition](https://mlsanity.com/paper/2003.05176/) (2020)
- [Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect](https://mlsanity.com/paper/2009.12991/) (2020)
- [What is the Effect of Importance Weighting in Deep Learning?](https://mlsanity.com/paper/1812.03372/) (2018)
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://mlsanity.com/paper/2006.07529/) (2020)
- [Distribution Alignment: A Unified Framework for Long-tail Visual Recognition](https://mlsanity.com/paper/2103.16370/) (2021)
- [CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2102.09559/) (2021)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective](https://mlsanity.com/paper/2003.10780/) (2020)

## Built on by (most cited first, 18 of 18)

- [CBA: Improving Online Continual Learning via Continual Bias Adaptor](https://mlsanity.com/paper/2308.06925/) (2023)
- [CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2403.10391/) (2024)
- [MedREK: Retrieval-Based Editing for Medical LLMs with Key-Aware Prompts](https://mlsanity.com/paper/2510.13500/) (2025)
- [Twice Class Bias Correction for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2312.16604/) (2023)
- [Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model](https://mlsanity.com/paper/2503.00884/) (2025)
- [Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning](https://mlsanity.com/paper/2407.18624/) (2024)
- [Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors from a Bi-level Optimization Perspective](https://mlsanity.com/paper/2408.13991/) (2024)
- [Marginal Debiased Network for Fair Visual Recognition](https://mlsanity.com/paper/2401.02150/) (2024)
- [Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification](https://mlsanity.com/paper/2401.07395/) (2024)
- [Sampling Control for Imbalanced Calibration in Semi-Supervised Learning](https://mlsanity.com/paper/2511.18773/) (2025)
- [Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning](https://mlsanity.com/paper/2401.04435/) (2024)
- [Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch](https://mlsanity.com/paper/2405.16093/) (2024)
- [Imbalanced Semi-Supervised Learning via Label Refinement and Threshold Adjustment](https://mlsanity.com/paper/2407.05370/) (2024)
- [Theory-inspired Label Shift Adaptation via Aligned Distribution Mixture](https://mlsanity.com/paper/2411.02047/) (2024)
- [SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples](https://mlsanity.com/paper/2501.06004/) (2025)
- [ULFine: Unbiased Lightweight Fine-tuning for Foundation-Model-Assisted Long-Tailed Semi-Supervised Learning](https://mlsanity.com/paper/2505.05062/) (2025)
- [CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels](https://mlsanity.com/paper/2507.11807/) (2025)
- [Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning](https://mlsanity.com/paper/2608.30699/) (2026)

## Abstract (arXiv)

Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data are balanced. However, such an assumption is far from realistic scenarios and thus severely limits the performance of current pseudo-labeling methods under the context of class-imbalance. To alleviate this problem, we design a bias adaptive classifier that targets the imbalanced SSL setups. The core idea is to automatically assimilate the training bias caused by class imbalance via the bias adaptive classifier, which is composed of a novel bias attractor and the original linear classifier. The bias attractor is designed as a light-weight residual network and optimized through a bi-level learning framework. Such a learning strategy enables the bias adaptive classifier to fit imbalanced training data, while the linear classifier can provide unbiased label prediction for each class. We conduct extensive experiments under various imbalanced semi-supervised setups, and the results demonstrate that our method can be applied to different pseudo-labeling models and is superior to current state-of-the-art methods.