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 51–75 of 308 papers

TitleStatusHype
Can AI help in screening Viral and COVID-19 pneumonia?Code1
GANSeg: Learning to Segment by Unsupervised Hierarchical Image GenerationCode1
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse InputCode1
Learning Data Augmentation Strategies for Object DetectionCode1
Prompt-Free Conditional Diffusion for Multi-object Image AugmentationCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
Unsupervised Data Augmentation for Consistency TrainingCode1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Improved Regularization of Convolutional Neural Networks with CutoutCode1
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairingCode0
Improved Mixed-Example Data AugmentationCode0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
Application of Facial Recognition using Convolutional Neural Networks for Entry Access ControlCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Batch Augmentation with Unimodal Fine-tuning for Multimodal LearningCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified