mlsanityData to 7 Oct 2026

You Only Cut Once: Boosting Data Augmentation with a Single Cut

arXiv 2201.12078 ↗First posted 28 Jan 2022cs.CV cs.LGcited by 22

Not on the map. Why?

Abstract (arXiv)

We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying YOCO improves the diversity of the augmentation per sample and encourages neural networks to recognize objects from partial information. YOCO enjoys the properties of parameter-free, easy usage, and boosting almost all augmentations for free. Thorough experiments are conducted to evaluate its effectiveness. We first demonstrate that YOCO can be seamlessly applied to varying data augmentations, neural network architectures, and brings performance gains on CIFAR and ImageNet classification tasks, sometimes surpassing conventional image-level augmentation by large margins. Moreover, we show YOCO benefits contrastive pre-training toward a more powerful representation that can be better transferred to multiple downstream tasks. Finally, we study a number of variants of YOCO and empirically analyze the performance for respective settings. Code is available at GitHub.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 56 of its 69 references that are in the map. First posted on arXiv 28 Jan 2022.

Builds on

56 of 69 references.

References · 56

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  7. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  8. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  9. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  10. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  11. 2017Mask R-CNNcited by 7,259
  12. 2016Densely Connected Convolutional Networkscited by 6,691
38 more
  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2016Wide Residual Networkscited by 3,057
  7. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  8. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  9. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  10. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  11. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  12. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  13. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  14. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  15. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  16. 2019Natural Adversarial Examplescited by 1,401
  17. 2017Random Erasing Data Augmentationcited by 1,235
  18. 2016Deep Networks with Stochastic Depthcited by 973
  19. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  20. 2021Multi-Stage Progressive Image Restorationcited by 813
  21. 2018AutoAugment: Learning Augmentation Policies from Datacited by 728
  22. 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 637
  23. 2014Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFscited by 552
  24. 2017Large Batch Training of Convolutional Networkscited by 529
  25. 2019Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Modelcited by 400
  26. 2017"Zero-Shot" Super-Resolution using Deep Internal Learningcited by 285
  27. 2018Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkcited by 263
  28. 2019Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNetcited by 260
  29. 2017Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detectioncited by 209
  30. 2020Torchattacks: A PyTorch Repository for Adversarial Attackscited by 156
  31. 2021PixMix: Dreamlike Pictures Comprehensively Improve Safety Measurescited by 131
  32. 2021Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversitycited by 127
  33. 2018GAN Augmentation: Augmenting Training Data using Generative Adversarial Networkscited by 107
  34. 2019Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentationcited by 105
  35. 2016Deep Feature Interpolation for Image Content Changescited by 80
  36. 2015Posterior calibration and exploratory analysis for natural language processing modelscited by 75
  37. 2020Regularizing Deep Networks with Semantic Data Augmentationcited by 73
  38. 2020Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategycited by 66

50 most cited of 56 All 56 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 7; 2025: 3; 2026: 3.

22 papers.

Sort:

By area:

  1. 2024DiffuseMix: Label-Preserving Data Augmentation with Diffusion Modelscited by 78
  2. 2023Prompt-In-Prompt Learning for Universal Image Restorationcited by 43
  3. 2023Image Data Augmentation Approaches: A Comprehensive Survey and Future directionscited by 23
  4. 2024A Survey on Mixup Augmentations and Beyondcited by 18
  5. 2024GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editingcited by 14
  6. 2022PointCaM: Cut-and-Mix for Open-Set Point Cloud Learningcited by 11
  7. 2025Context-guided Responsible Data Augmentation with Diffusion Modelscited by 9
  8. 2022CropMix: Sampling a Rich Input Distribution via Multi-Scale Croppingcited by 8
  9. 2022What Images are More Memorable to Machines?cited by 5
  10. 2023Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learningcited by 5
  11. 2023Towards Understanding How Self-training Tolerates Data Backdoor Poisoningcited by 4
  12. 2023SASSL: Enhancing Self-Supervised Learning via Neural Style Transfercited by 2
10 more
  1. 2024NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detectioncited by 2
  2. 2023DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejectioncited by 1
  3. 2024You Only Need Half: Boosting Data Augmentation by Using Partial Contentcited by 1
  4. 2024Dynamic Feature Learning and Matching for Class-Incremental Learningcited by 1
  5. 2025Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentationcited by 1
  6. 2025Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Modelscited by 1
  7. 2026InstructMixup: Instruction-Guided Salient Patch Editing for Robust Data Augmentationcited by 1
  8. 2024Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Trainingcited by 0
  9. 2026HACMatch Semi-Supervised Rotation Regression with Hardness-Aware Curriculum Pseudo Labelingcited by 0
  10. 2026AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognitioncited by 0