Image Augmentation
98 papers with code • 1 benchmarks • 1 datasets
Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.
Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing
( Image credit: Kornia )
Benchmarks
These leaderboards are used to track progress in Image Augmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Image Augmentation models and implementations
Intel Image Classification
Most implemented papers
AutoAugment: Learning Augmentation Policies from Data
In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
Improved Regularization of Convolutional Neural Networks with Cutout
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.
Unsupervised Data Augmentation for Consistency Training
google-research/uda
•
•
NeurIPS 2020
In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.
Random Erasing Data Augmentation
zhunzhong07/Random-Erasing
•
•
In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
Fast AutoAugment
kakaobrain/fast-autoaugment
•
•
NeurIPS 2019
Data augmentation is an essential technique for improving generalization ability of deep learning models.
Augmentor: An Image Augmentation Library for Machine Learning
The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting.
Learning Data Augmentation Strategies for Object Detection
Importantly, the best policy found on COCO may be transferred unchanged to other detection datasets and models to improve predictive accuracy.
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3. 6 mask AP on rare categories.
Albumentations: fast and flexible image augmentations
We provide examples of image augmentations for different computer vision tasks and show that Albumentations is faster than other commonly used image augmentation tools on the most of commonly used image transformations.
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
kornia/kornia
•
•
This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems.
