SOTAVerified

Image 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

( Image credit: Kornia )

Papers

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Showing 76–100 of 308 papers

TitleStatusHype
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
Isometric Transformations for Image Augmentation in Mueller Matrix PolarimetryCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification TasksCode0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
An Empirical Study of Validating Synthetic Data for Text-Based Person RetrievalCode0
Improving Fairness using Vision-Language Driven Image AugmentationCode0
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairingCode0
DenseNet Models for Tiny ImageNet ClassificationCode0
Improved Mixed-Example Data AugmentationCode0
Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image AugmentationCode0
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
Augmentor: An Image Augmentation Library for Machine LearningCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
Population Based Augmentation: Efficient Learning of Augmentation Policy SchedulesCode0
Data Augmentation via Levy ProcessesCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Data Augmentation using Random Image Cropping and Patching for Deep CNNsCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer SensorsCode0
Few-Shot Learning for Image Classification of Common FloraCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified