Browse SoTA > Computer Vision > Data Augmentation > Image Augmentation

Image Augmentation

34 papers with code · Computer Vision
Subtask of Data Augmentation

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

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Greatest papers with code

AutoAugment: Learning Augmentation Policies from Data

24 May 2018tensorflow/models

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

Albumentations: fast and flexible image augmentations

18 Sep 2018albu/albumentations

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

Random Erasing Data Augmentation

16 Aug 2017rwightman/pytorch-image-models

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

IMAGE AUGMENTATION IMAGE CLASSIFICATION OBJECT DETECTION PERSON RE-IDENTIFICATION

Learning Data Augmentation Strategies for Object Detection

ECCV 2020 tensorflow/tpu

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 OBJECT DETECTION

Unsupervised Data Augmentation for Consistency Training

NeurIPS 2020 google-research/uda

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 TEXT CLASSIFICATION TRANSFER LEARNING

Fast AutoAugment

NeurIPS 2019 kakaobrain/fast-autoaugment

Data augmentation is an essential technique for improving generalization ability of deep learning models.

IMAGE AUGMENTATION IMAGE CLASSIFICATION

CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents

27 Apr 2020DevashishPrasad/CascadeTabNet

In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model.

IMAGE AUGMENTATION TABLE DETECTION TRANSFER LEARNING

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

14 May 2019arcelien/pba

A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.

IMAGE AUGMENTATION

Improved Regularization of Convolutional Neural Networks with Cutout

15 Aug 2017uoguelph-mlrg/Cutout

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

DOMAIN GENERALIZATION IMAGE AUGMENTATION SEMI-SUPERVISED IMAGE CLASSIFICATION