Image Augmentation

92 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 )


Use these libraries to find Image Augmentation models and implementations
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Most implemented papers

AutoAugment: Learning Augmentation Policies from Data

tensorflow/models 24 May 2018

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

uoguelph-mlrg/Cutout 15 Aug 2017

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 16 Aug 2017

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

Evizero/Augmentor.jl 11 Aug 2017

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

tensorflow/tpu ECCV 2020

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

tensorflow/tpu CVPR 2021

Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3. 6 mask AP on rare categories.

Albumentations: fast and flexible image augmentations

albu/albumentations 18 Sep 2018

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 5 Oct 2019

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.