# CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition

arXiv: https://arxiv.org/abs/2302.05499 · HTML: https://mlsanity.com/paper/2302.05499/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 63 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 30 of 49)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [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)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Long-tailed Recognition by Routing Diverse Distribution-Aware Experts](https://mlsanity.com/paper/2010.01809/) (2020)
- [Fast AutoAugment](https://mlsanity.com/paper/1905.00397/) (2019)
- [Parametric Contrastive Learning](https://mlsanity.com/paper/2107.12028/) (2021)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification](https://mlsanity.com/paper/2001.01536/) (2020)
- [MixUp as Locally Linear Out-Of-Manifold Regularization](https://mlsanity.com/paper/1809.02499/) (2018)
- [Remix: Rebalanced Mixup](https://mlsanity.com/paper/2007.03943/) (2020)

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

- [Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts](https://mlsanity.com/paper/2309.10019/) (2023)
- [Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery](https://mlsanity.com/paper/2310.19776/) (2023)
- [How Re-sampling Helps for Long-Tail Learning?](https://mlsanity.com/paper/2310.18236/) (2023)
- [A Systematic Review on Long-Tailed Learning](https://mlsanity.com/paper/2408.00483/) (2024)
- [A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization](https://mlsanity.com/paper/2310.04752/) (2023)
- [Revisiting Adversarial Training under Long-Tailed Distributions](https://mlsanity.com/paper/2403.10073/) (2024)
- [Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning](https://mlsanity.com/paper/2308.04142/) (2023)
- [Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion](https://mlsanity.com/paper/2410.13674/) (2024)
- [Subgraph Federated Learning for Local Generalization](https://mlsanity.com/paper/2503.03995/) (2025)
- [Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning](https://mlsanity.com/paper/2310.10090/) (2023)
- [LIFT+: Lightweight Fine-Tuning for Long-Tail Learning](https://mlsanity.com/paper/2504.13282/) (2025)
- [DiffuLT: How to Make Diffusion Model Useful for Long-tail Recognition](https://mlsanity.com/paper/2403.05170/) (2024)
- [Free-Grained Hierarchical Visual Recognition](https://mlsanity.com/paper/2510.14737/) (2025)
- [Long-Tailed Backdoor Attack Using Dynamic Data Augmentation Operations](https://mlsanity.com/paper/2410.12955/) (2024)
- [Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classification](https://mlsanity.com/paper/2505.08173/) (2025)
- [Data-Centric Long-Tailed Image Recognition](https://mlsanity.com/paper/2311.01744/) (2023)
- [Revisiting Long-Tailed Learning: Insights from an Architectural Perspective](https://mlsanity.com/paper/2411.06098/) (2024)
- [Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data](https://mlsanity.com/paper/2510.08179/) (2025)
- [Reframing Long-Tailed Learning via Loss Landscape Geometry](https://mlsanity.com/paper/2603.21217/) (2026)
- [Decision Boundary-aware Generation for Long-tailed Learning](https://mlsanity.com/paper/2605.01468/) (2026)
- [Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions](https://mlsanity.com/paper/2605.03221/) (2026)
- [Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation](https://mlsanity.com/paper/2304.09347/) (2023)
- [Deep Minimax Classifiers for Imbalanced Datasets with a Small Number of Minority Samples](https://mlsanity.com/paper/2502.16948/) (2025)
- [Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning](https://mlsanity.com/paper/2504.03687/) (2025)
- [Reducing Class Bias In Data-Balanced Datasets Through Hardness-Based Resampling](https://mlsanity.com/paper/2504.07031/) (2025)
- [PI-H2T: Enhancing Long-Tailed Visual Recognition with Permutation-Invariant and Head-to-Tail Feature Fusion](https://mlsanity.com/paper/2506.00625/) (2025)
- [Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers](https://mlsanity.com/paper/2507.11852/) (2025)
- [Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment](https://mlsanity.com/paper/2605.02183/) (2026)
- [Why Not Hyperparameter-Friendly Optimisation? A Monotonic Adaptive Norm Rescaling Approach For Long-Tailed Recognition](https://mlsanity.com/paper/2606.02526/) (2026)
- [Gen2Balance: Generative Balancing for Long-Tailed Video Action Recognition](https://mlsanity.com/paper/2606.22416/) (2026)

## Abstract (arXiv)

Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets. To mitigate this problem, many approaches have aimed to balance among given classes by re-weighting or re-sampling training samples. These re-balancing methods increase the impact of minority classes and reduce the influence of majority classes on the output of models. However, the extracted representations may be of poor quality owing to the limited number of minority samples. To handle this restriction, several methods have been developed that increase the representations of minority samples by leveraging the features of the majority samples. Despite extensive recent studies, no deep analysis has been conducted on determination of classes to be augmented and strength of augmentation has been conducted. In this study, we first investigate the correlation between the degree of augmentation and class-wise performance, and find that the proper degree of augmentation must be allocated for each class to mitigate class imbalance problems. Motivated by this finding, we propose a simple and efficient novel curriculum, which is designed to find the appropriate per-class strength of data augmentation, called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA can simply be integrated into existing long-tailed recognition methods. We present the results of experiments showing that CUDA effectively achieves better generalization performance compared to the state-of-the-art method on various imbalanced datasets such as CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018.