Methods › Computer Vision › Image Data Augmentation › Cutout

Cutout

64 papers tagged archive 2025-07-28

Introduced by Terrance DeVries et al. in Improved Regularization of Convolutional Neural Networks with Cutout

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Cutout is an image augmentation and regularization technique that randomly masks out square regions of input during training. and can be used to improve the robustness and overall performance of convolutional neural networks. The main motivation for cutout comes from the problem of object occlusion, which is commonly encountered in many computer vision tasks, such as object recognition, tracking, or human pose estimation. By generating new images which simulate occluded examples, we not only better prepare the model for encounters with occlusions in the real world, but the model also learns to take more of the image context into consideration when making decisions

PaperSource

Papers archive 2025-07-28

30 shown of 64, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 94 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification22
Data Augmentation19
Neural Architecture Search17
image-classification13
Object Detection7
GPU6
General Classification6
object-detection6
Domain Generalization4
Image Augmentation4
Diversity3
Object3
Reinforcement Learning3
Reinforcement Learning (RL)3
Retrieval3
Segmentation3
Semantic Segmentation3
reinforcement-learning3
Classification2
Deep Learning2

Usage over time archive 2025-07-28

Papers per year tagged with Cutout: 2017 to 2025, peak 20 20 0 2017: 1 paper 2017 2018: 3 papers 2018 2019: 10 papers 2019 2020: 20 papers 2020 2021: 8 papers 2021 2022: 10 papers 2022 2023: 6 papers 2023 2024: 5 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (64 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Data Augmentation

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