Image Data Augmentation
 
Edit

Computer Vision • 45 methods

Image Data Augmentation refers to a class of methods that augment an image dataset to increase the effective size of the training set, or as a form of regularization to help the network learn more effective representations.

Subcategories

Methods
Add a Method

Method Year Papers
Mixup
mixup: Beyond Empirical Risk Minimization
2017 534
GPS
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
2020 507
Random Resized Crop
2000 264
ColorJitter
2000 220
Random Gaussian Blur
2000 218
CutMix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
2019 177
Random Horizontal Flip
2000 78
Cutout
Improved Regularization of Convolutional Neural Networks with Cutout
2017 60
RandAugment
RandAugment: Practical automated data augmentation with a reduced search space
2019 58
AutoAugment
AutoAugment: Learning Augmentation Policies from Data
2018 57
ADA
Training Generative Adversarial Networks with Limited Data
2020 49
Copy-Paste
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
2020 33
AugMix
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
2019 16
Random Erasing
Random Erasing Data Augmentation
2017 14
APA
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
2021 12
Random Scaling
2000 10
Image Scale Augmentation
2000 9
PAA
Local Patch AutoAugment with Multi-Agent Collaboration
2021 8
Fast AutoAugment
Fast AutoAugment
2019 6
DG-Net
Joint Discriminative and Generative Learning for Person Re-identification
2019 4
DiffAugment
Differentiable Augmentation for Data-Efficient GAN Training
2020 3
Random Grayscale
2000 3
Adversarial Color Enhancement
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter
2020 3
GridMask
GridMask Data Augmentation
2020 3
Population Based Augmentation
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
2019 3
LFPNet (TTA)
$F$, $B$, Alpha Matting
2020 2
MaxUp
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
2020 2
Local Mixup
Preventing Manifold Intrusion with Locality: Local Mixup
2022 2
FMix
FMix: Enhancing Mixed Sample Data Augmentation
2020 2
SuperpixelGridMasks
2000 2
3-Augment
DeiT III: Revenge of the ViT
2022 1
InstaBoost
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
2019 1
R-Mix
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
2022 1
Handwritten OCR
Handwritten image augmentation
2023 1
OA-Mix
Object-Aware Domain Generalization for Object Detection
2023 1
Sample Redistribution
Sample and Computation Redistribution for Efficient Face Detection
2021 1
Batchboost
batchboost: regularization for stabilizing training with resistance to underfitting & overfitting
2020 1
Object Dropout
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning
2021 1
CutBlur
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
2020 1
RandomRotate
2000 1
imagemorph
2000 0
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy