# A Systematic Review on Long-Tailed Learning

arXiv: https://arxiv.org/abs/2408.00483 · HTML: https://mlsanity.com/paper/2408.00483/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 156 of its 237 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 30 of 156)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [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)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [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)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [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)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Equalization Loss for Long-Tailed Object Recognition](https://mlsanity.com/paper/2003.05176/) (2020)
- [Prior Guided Feature Enrichment Network for Few-Shot Segmentation](https://mlsanity.com/paper/2008.01449/) (2020)
- [Long-tailed Recognition by Routing Diverse Distribution-Aware Experts](https://mlsanity.com/paper/2010.01809/) (2020)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)

## Built on by (most cited first, 28 of 28)

- [Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification](https://mlsanity.com/paper/2505.01660/) (2025)
- [From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration](https://mlsanity.com/paper/2503.12821/) (2025)
- [Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie](https://mlsanity.com/paper/2601.12554/) (2026)
- [Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation](https://mlsanity.com/paper/2509.19112/) (2025)
- [Targeted Deep Learning System Boundary Testing](https://mlsanity.com/paper/2408.06258/) (2024)
- [Robust Policy Expansion for Offline-to-Online RL under Diverse Data Corruption](https://mlsanity.com/paper/2509.24748/) (2025)
- [Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling](https://mlsanity.com/paper/2511.18858/) (2025)
- [Prototype Guided Post-pretraining for Single-Cell Representation Learning](https://mlsanity.com/paper/2605.07938/) (2026)
- [Enhancing Features in Long-tailed Data Using Large Vision Model](https://mlsanity.com/paper/2504.10852/) (2025)
- [Emergent Specialization: Rare Token Neurons in Language Models](https://mlsanity.com/paper/2505.12822/) (2025)
- [From Data to Knowledge: Evaluating How Efficiently Language Models Learn Facts](https://mlsanity.com/paper/2506.16912/) (2025)
- [Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts](https://mlsanity.com/paper/2507.16946/) (2025)
- [Distributed Specialization: Rare-Token Neurons in Large Language Models](https://mlsanity.com/paper/2509.21163/) (2025)
- [From Memorization to Generalization: Fine-Tuning Large Language Models for Biomedical Term-to-Identifier Normalization](https://mlsanity.com/paper/2510.19036/) (2025)
- [REMIND: Rethinking Medical High-Modality Learning under Missingness--A Long-Tailed Distribution Perspective](https://mlsanity.com/paper/2603.00046/) (2026)
- [On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data](https://mlsanity.com/paper/2411.04586/) (2024)
- [Multi-Agent Causal Reasoning System for Error Pattern Rule Automation in Vehicles](https://mlsanity.com/paper/2602.01155/) (2026)
- [Neural Prior Estimation: Learning Class Priors from Latent Representations](https://mlsanity.com/paper/2602.17853/) (2026)
- [Dual-Imbalance Continual Learning for Real-World Food Recognition](https://mlsanity.com/paper/2603.29133/) (2026)
- [CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning](https://mlsanity.com/paper/2605.01309/) (2026)
- [Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment](https://mlsanity.com/paper/2605.02183/) (2026)
- [DA-UCT: Self-Supervised Domain-Adaptive Ultrasound Computed Tomography for Rapid Musculoskeletal Sound Speed Reconstruction](https://mlsanity.com/paper/2605.25024/) (2026)
- [Generalised Balanced Softmax: A Finite-Data Perspective on Logit Adjustment for Long-Tailed Recognition](https://mlsanity.com/paper/2607.22258/) (2026)
- [GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment](https://mlsanity.com/paper/2608.26724/) (2026)
- [Beyond Homoscedasticity: Decoupled Uncertainty Optimization for Deep Imbalanced Regression](https://mlsanity.com/paper/2609.04995/) (2026)
- [Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics](https://mlsanity.com/paper/2609.38768/) (2026)
- [A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform](https://mlsanity.com/paper/2610.00926/) (2026)
- [Learning from imperfect teachers for low-resource acoustic generalization](https://mlsanity.com/paper/2610.05256/) (2026)

## Abstract (arXiv)

Long-tailed data is a special type of multi-class imbalanced data with a very large amount of minority/tail classes that have a very significant combined influence. Long-tailed learning aims to build high-performance models on datasets with long-tailed distributions, which can identify all the classes with high accuracy, in particular the minority/tail classes. It is a cutting-edge research direction that has attracted a remarkable amount of research effort in the past few years. In this paper, we present a comprehensive survey of latest advances in long-tailed visual learning. We first propose a new taxonomy for long-tailed learning, which consists of eight different dimensions, including data balancing, neural architecture, feature enrichment, logits adjustment, loss function, bells and whistles, network optimization, and post hoc processing techniques. Based on our proposed taxonomy, we present a systematic review of long-tailed learning methods, discussing their commonalities and alignable differences. We also analyze the differences between imbalance learning and long-tailed learning approaches. Finally, we discuss prospects and future directions in this field.