# Label-Noise Learning with Intrinsically Long-Tailed Data

arXiv: https://arxiv.org/abs/2208.09833 · HTML: https://mlsanity.com/paper/2208.09833/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 63 references that are in the map.
- First posted on arXiv 21 Aug 2022.

## Builds on (most cited first, 30 of 46)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [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)
- [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)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [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)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2007.10740/) (2020)
- [Equalization Loss for Long-Tailed Object Recognition](https://mlsanity.com/paper/2003.05176/) (2020)
- [Long-tailed Recognition by Routing Diverse Distribution-Aware Experts](https://mlsanity.com/paper/2010.01809/) (2020)
- [Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations](https://mlsanity.com/paper/2110.12088/) (2021)
- [Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise](https://mlsanity.com/paper/1802.05300/) (2018)
- [Improving Calibration for Long-Tailed Recognition](https://mlsanity.com/paper/2104.00466/) (2021)
- [Does label smoothing mitigate label noise?](https://mlsanity.com/paper/2003.02819/) (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)
- [Remix: Rebalanced Mixup](https://mlsanity.com/paper/2007.03943/) (2020)
- [Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning](https://mlsanity.com/paper/2006.07805/) (2020)
- [A Survey of Label-noise Representation Learning: Past, Present and Future](https://mlsanity.com/paper/2011.04406/) (2020)
- [Selective-Supervised Contrastive Learning with Noisy Labels](https://mlsanity.com/paper/2203.04181/) (2022)
- [Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning](https://mlsanity.com/paper/2007.08844/) (2020)

## Built on by (most cited first, 17 of 17)

- [TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection](https://mlsanity.com/paper/2504.02775/) (2025)
- [Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples](https://mlsanity.com/paper/2502.08227/) (2025)
- [On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction](https://mlsanity.com/paper/2402.18061/) (2024)
- [Extracting Clean and Balanced Subset for Noisy Long-tailed Classification](https://mlsanity.com/paper/2404.06795/) (2024)
- [Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss](https://mlsanity.com/paper/2211.10906/) (2022)
- [Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels](https://mlsanity.com/paper/2407.16802/) (2024)
- [Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark](https://mlsanity.com/paper/2410.15772/) (2024)
- [Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data](https://mlsanity.com/paper/2510.08179/) (2025)
- [CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels](https://mlsanity.com/paper/2605.23254/) (2026)
- [Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss](https://mlsanity.com/paper/2310.08206/) (2023)
- [Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity](https://mlsanity.com/paper/2403.02363/) (2024)
- [Learning from Noisy Labels for Long-tailed Data via Optimal Transport](https://mlsanity.com/paper/2408.03977/) (2024)
- [GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning](https://mlsanity.com/paper/2412.14561/) (2024)
- [Soft causal learning for generalized molecule property prediction: An environment perspective](https://mlsanity.com/paper/2505.06283/) (2025)
- [Learning from Imperfect Text Guidance: Robust Long-Tail Visual Recognition with High-Noise Label](https://mlsanity.com/paper/2604.23125/) (2026)
- [Benchmarking Instance-Dependent Label Noise with Controlled Corruptions](https://mlsanity.com/paper/2606.14965/) (2026)
- [Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shift](https://mlsanity.com/paper/2606.22991/) (2026)

## Abstract (arXiv)

Label noise is one of the key factors that lead to the poor generalization of deep learning models. Existing label-noise learning methods usually assume that the ground-truth classes of the training data are balanced. However, the real-world data is often imbalanced, leading to the inconsistency between observed and intrinsic class distribution with label noises. In this case, it is hard to distinguish clean samples from noisy samples on the intrinsic tail classes with the unknown intrinsic class distribution. In this paper, we propose a learning framework for label-noise learning with intrinsically long-tailed data. Specifically, we propose two-stage bi-dimensional sample selection (TABASCO) to better separate clean samples from noisy samples, especially for the tail classes. TABASCO consists of two new separation metrics that complement each other to compensate for the limitation of using a single metric in sample separation. Extensive experiments on benchmarks demonstrate the effectiveness of our method. Our code is available at https://github.com/Wakings/TABASCO.