# ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

arXiv: https://arxiv.org/abs/2110.10368 · HTML: https://mlsanity.com/paper/2110.10368/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 39 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [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)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [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)
- [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)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2007.10740/) (2020)
- [S4L: Self-Supervised Semi-Supervised Learning](https://mlsanity.com/paper/1905.03670/) (2019)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://mlsanity.com/paper/2006.07529/) (2020)
- [CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2102.09559/) (2021)
- [Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective](https://mlsanity.com/paper/2003.10780/) (2020)
- [M2m: Imbalanced Classification via Major-to-minor Translation](https://mlsanity.com/paper/2004.00431/) (2020)
- [Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning](https://mlsanity.com/paper/2007.08844/) (2020)
- [Adversarial Dropout for Supervised and Semi-supervised Learning](https://mlsanity.com/paper/1707.03631/) (2017)
- [Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients](https://mlsanity.com/paper/2009.13447/) (2020)

## Built on by (most cited first, 30 of 65)

- [FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning](https://mlsanity.com/paper/2205.07246/) (2022)
- [SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning](https://mlsanity.com/paper/2301.10921/) (2023)
- [Semi-Supervised and Unsupervised Deep Visual Learning: A Survey](https://mlsanity.com/paper/2208.11296/) (2022)
- [CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2112.04564/) (2021)
- [Revisiting Consistency Regularization for Semi-Supervised Learning](https://mlsanity.com/paper/2112.05825/) (2021)
- [A Systematic Review on Long-Tailed Learning](https://mlsanity.com/paper/2408.00483/) (2024)
- [On Modality Bias Recognition and Reduction](https://mlsanity.com/paper/2202.12690/) (2022)
- [On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning](https://mlsanity.com/paper/2306.04934/) (2023)
- [Imbalanced Semi-supervised Learning with Bias Adaptive Classifier](https://mlsanity.com/paper/2207.13856/) (2022)
- [Label-Noise Learning with Intrinsically Long-Tailed Data](https://mlsanity.com/paper/2208.09833/) (2022)
- [SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning](https://mlsanity.com/paper/2402.13505/) (2024)
- [Three Heads Are Better Than One: Complementary Experts for Long-Tailed Semi-supervised Learning](https://mlsanity.com/paper/2312.15702/) (2023)
- [An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2211.11086/) (2022)
- [InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning](https://mlsanity.com/paper/2303.07269/) (2023)
- [Towards Distribution-Agnostic Generalized Category Discovery](https://mlsanity.com/paper/2310.01376/) (2023)
- [Boosting Semi-Supervised Learning by bridging high and low-confidence predictions](https://mlsanity.com/paper/2308.07509/) (2023)
- [CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2403.10391/) (2024)
- [Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition](https://mlsanity.com/paper/2410.06109/) (2024)
- [Transfer and Share: Semi-Supervised Learning from Long-Tailed Data](https://mlsanity.com/paper/2205.13358/) (2022)
- [BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning](https://mlsanity.com/paper/2403.12986/) (2024)
- [SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning](https://mlsanity.com/paper/2310.15787/) (2023)
- [Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper Calibration](https://mlsanity.com/paper/2306.04621/) (2023)
- [A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning](https://mlsanity.com/paper/2505.16341/) (2025)
- [Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study](https://mlsanity.com/paper/2306.10494/) (2023)
- [Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization](https://mlsanity.com/paper/2401.06975/) (2024)
- [BEM: Balanced and Entropy-based Mix for Long-Tailed Semi-Supervised Learning](https://mlsanity.com/paper/2404.01179/) (2024)
- [ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering](https://mlsanity.com/paper/2407.16931/) (2024)
- [A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise](https://mlsanity.com/paper/2206.07277/) (2022)
- [UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection](https://mlsanity.com/paper/2312.01225/) (2023)
- [Twice Class Bias Correction for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2312.16604/) (2023)

## Abstract (arXiv)

Existing semi-supervised learning (SSL) algorithms typically assume class-balanced datasets, although the class distributions of many real-world datasets are imbalanced. In general, classifiers trained on a class-imbalanced dataset are biased toward the majority classes. This issue becomes more problematic for SSL algorithms because they utilize the biased prediction of unlabeled data for training. However, traditional class-imbalanced learning techniques, which are designed for labeled data, cannot be readily combined with SSL algorithms. We propose a scalable class-imbalanced SSL algorithm that can effectively use unlabeled data, while mitigating class imbalance by introducing an auxiliary balanced classifier (ABC) of a single layer, which is attached to a representation layer of an existing SSL algorithm. The ABC is trained with a class-balanced loss of a minibatch, while using high-quality representations learned from all data points in the minibatch using the backbone SSL algorithm to avoid overfitting and information loss.Moreover, we use consistency regularization, a recent SSL technique for utilizing unlabeled data in a modified way, to train the ABC to be balanced among the classes by selecting unlabeled data with the same probability for each class. The proposed algorithm achieves state-of-the-art performance in various class-imbalanced SSL experiments using four benchmark datasets.