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Image Augmentation

28 papers with code · Computer Vision
Subtask of Data Augmentation

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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

Learning Data Augmentation Strategies for Object Detection

26 Jun 2019tensorflow/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

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

Unsupervised Data Augmentation for Consistency Training

arXiv 2019 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

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.

IMAGE AUGMENTATION SEMI-SUPERVISED IMAGE CLASSIFICATION

Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition

14 Mar 2020Canjie-Luo/Scene-Text-Image-Transformer

An agent network learns from the output of the recognition network and controls the fiducial points to generate more proper training samples for the recognition network.

IMAGE AUGMENTATION