Image Augmentation

58 papers with code • 0 benchmarks • 0 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 )

Datasets


Greatest papers with code

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.

Fine-Grained Image Classification Image Augmentation

Random Erasing Data Augmentation

rwightman/pytorch-image-models 16 Aug 2017

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).

General Classification Image Augmentation +3

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.

Image Augmentation

Differentiable Data Augmentation with Kornia

arraiyopensource/kornia 19 Nov 2020

In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors.

Image Augmentation Image Manipulation +1

A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

kornia/kornia 21 Sep 2020

This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems.

Edge Detection Image Augmentation +1

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.

Edge Detection Image Augmentation +9

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.

Image Augmentation Image Classification +1

Improved Regularization of Convolutional Neural Networks with Cutout

PaddlePaddle/PaddleClas 15 Aug 2017

Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.

Domain Generalization Image Augmentation +1

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.

Image Augmentation Semi-Supervised Image Classification +2