# Provable Benefit of Cutout and CutMix for Feature Learning

arXiv: https://arxiv.org/abs/2410.23672 · HTML: https://mlsanity.com/paper/2410.23672/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 30 of 31)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning](https://mlsanity.com/paper/2012.09816/) (2020)
- [Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup](https://mlsanity.com/paper/2009.06962/) (2020)
- [How Does Mixup Help With Robustness and Generalization?](https://mlsanity.com/paper/2010.04819/) (2020)
- [Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity](https://mlsanity.com/paper/2102.03065/) (2021)
- [GridMask Data Augmentation](https://mlsanity.com/paper/2001.04086/) (2020)
- [A Kernel Theory of Modern Data Augmentation](https://mlsanity.com/paper/1803.06084/) (2018)
- [Benign Overfitting in Two-layer Convolutional Neural Networks](https://mlsanity.com/paper/2202.06526/) (2022)
- [On Mixup Regularization](https://mlsanity.com/paper/2006.06049/) (2020)
- [Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data](https://mlsanity.com/paper/2202.05928/) (2022)
- [When and How Mixup Improves Calibration](https://mlsanity.com/paper/2102.06289/) (2021)
- [Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization](https://mlsanity.com/paper/2108.11371/) (2021)
- [On the Generalization Effects of Linear Transformations in Data Augmentation](https://mlsanity.com/paper/2005.00695/) (2020)
- [Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data](https://mlsanity.com/paper/2210.07082/) (2022)
- [Towards understanding how momentum improves generalization in deep learning](https://mlsanity.com/paper/2207.05931/) (2022)
- [Why Does Sharpness-Aware Minimization Generalize Better Than SGD?](https://mlsanity.com/paper/2310.07269/) (2023)
- [A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective](https://mlsanity.com/paper/2208.09913/) (2022)
- [Towards Understanding the Data Dependency of Mixup-style Training](https://mlsanity.com/paper/2110.07647/) (2021)
- [The Benefits of Mixup for Feature Learning](https://mlsanity.com/paper/2303.08433/) (2023)
- [Does Data Augmentation Lead to Positive Margin?](https://mlsanity.com/paper/1905.03177/) (2019)
- [Data Augmentation as Feature Manipulation](https://mlsanity.com/paper/2203.01572/) (2022)
- [Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data](https://mlsanity.com/paper/2310.18935/) (2023)
- [How Data Augmentation affects Optimization for Linear Regression](https://mlsanity.com/paper/2010.11171/) (2020)
- [A Group-Theoretic Framework for Data Augmentation](https://mlsanity.com/paper/1907.10905/) (2019)
- [Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup](https://mlsanity.com/paper/2210.13512/) (2022)
- [Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons](https://mlsanity.com/paper/2306.13926/) (2023)
- [Provable Benefit of Mixup for Finding Optimal Decision Boundaries](https://mlsanity.com/paper/2306.00267/) (2023)
- [On the Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics](https://mlsanity.com/paper/2306.01271/) (2023)

## Built on by (most cited first, 4 of 4)

- [Reperio-rPPG: Relational Temporal Graph Neural Networks for Periodicity Learning in Remote Physiological Measurement](https://mlsanity.com/paper/2511.05946/) (2025)
- [Enabling Validation for Robust Few-Shot Recognition](https://mlsanity.com/paper/2506.04713/) (2025)
- [NSegment : Label-specific Deformations for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2504.19634/) (2025)
- [The Impact of CutMix on Reliability and Robustness in Semantic Segmentation](https://mlsanity.com/paper/2608.18715/) (2026)

## Abstract (arXiv)

Patch-level data augmentation techniques such as Cutout and CutMix have demonstrated significant efficacy in enhancing the performance of vision tasks. However, a comprehensive theoretical understanding of these methods remains elusive. In this paper, we study two-layer neural networks trained using three distinct methods: vanilla training without augmentation, Cutout training, and CutMix training. Our analysis focuses on a feature-noise data model, which consists of several label-dependent features of varying rarity and label-independent noises of differing strengths. Our theorems demonstrate that Cutout training can learn low-frequency features that vanilla training cannot, while CutMix training can learn even rarer features that Cutout cannot capture. From this, we establish that CutMix yields the highest test accuracy among the three. Our novel analysis reveals that CutMix training makes the network learn all features and noise vectors "evenly" regardless of the rarity and strength, which provides an interesting insight into understanding patch-level augmentation.