# Label-Aware Distribution Calibration for Long-tailed Classification arXiv: https://arxiv.org/abs/2111.04901 ยท HTML: https://mlsanity.com/paper/2111.04901/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 38 of its 55 references that are in the map. - First posted on arXiv 9 Nov 2021. ## Builds on (most cited first, 30 of 38) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017) - [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016) - [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019) - [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) - [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) - [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020) - [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017) - [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) - [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016) - [Size-Independent Sample Complexity of Neural Networks](https://mlsanity.com/paper/1712.06541/) (2017) - [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) - [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) - [Improving Calibration for Long-Tailed Recognition](https://mlsanity.com/paper/2104.00466/) (2021) - [Distribution Alignment: A Unified Framework for Long-tail Visual Recognition](https://mlsanity.com/paper/2103.16370/) (2021) - [Disentangling Label Distribution for Long-tailed Visual Recognition](https://mlsanity.com/paper/2012.00321/) (2020) - [Free Lunch for Few-shot Learning: Distribution Calibration](https://mlsanity.com/paper/2101.06395/) (2021) - [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014) - [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016) - [Remix: Rebalanced Mixup](https://mlsanity.com/paper/2007.03943/) (2020) - [Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective](https://mlsanity.com/paper/2003.10780/) (2020) - [Feature Space Augmentation for Long-Tailed Data](https://mlsanity.com/paper/2008.03673/) (2020) - [M2m: Imbalanced Classification via Major-to-minor Translation](https://mlsanity.com/paper/2004.00431/) (2020) ## Built on by (most cited first, 13 of 13) - [A Survey on Long-Tailed Visual Recognition](https://mlsanity.com/paper/2205.13775/) (2022) - [A Systematic Review on Long-Tailed Learning](https://mlsanity.com/paper/2408.00483/) (2024) - [When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method](https://mlsanity.com/paper/2211.10955/) (2022) - [Generative Oversampling for Imbalanced Data via Majority-Guided VAE](https://mlsanity.com/paper/2302.10910/) (2023) - [Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning](https://mlsanity.com/paper/2310.10090/) (2023) - [Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition](https://mlsanity.com/paper/2211.13471/) (2022) - [Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation](https://mlsanity.com/paper/2203.00452/) (2022) - [Data-Centric Long-Tailed Image Recognition](https://mlsanity.com/paper/2311.01744/) (2023) - [Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation](https://mlsanity.com/paper/2503.11140/) (2025) - [Mixture of Balanced Information Bottlenecks for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2509.01804/) (2025) - [Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration](https://mlsanity.com/paper/2604.16817/) (2026) - [Learning from Noisy Labels for Long-tailed Data via Optimal Transport](https://mlsanity.com/paper/2408.03977/) (2024) - [Distilling Balanced Knowledge from a Biased Teacher](https://mlsanity.com/paper/2506.18496/) (2025) ## Abstract (arXiv) Real-world data usually present long-tailed distributions. Training on imbalanced data tends to render neural networks perform well on head classes while much worse on tail classes. The severe sparseness of training instances for the tail classes is the main challenge, which results in biased distribution estimation during training. Plenty of efforts have been devoted to ameliorating the challenge, including data re-sampling and synthesizing new training instances for tail classes. However, no prior research has exploited the transferable knowledge from head classes to tail classes for calibrating the distribution of tail classes. In this paper, we suppose that tail classes can be enriched by similar head classes and propose a novel distribution calibration approach named as label-Aware Distribution Calibration LADC. LADC transfers the statistics from relevant head classes to infer the distribution of tail classes. Sampling from calibrated distribution further facilitates re-balancing the classifier. Experiments on both image and text long-tailed datasets demonstrate that LADC significantly outperforms existing methods.The visualization also shows that LADC provides a more accurate distribution estimation.