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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 308 papers

TitleStatusHype
Kornia: an Open Source Differentiable Computer Vision Library for PyTorchCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic ReviewCode1
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopyCode1
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning ModelsCode1
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse InputCode1
Unsupervised Data Augmentation for Consistency TrainingCode1
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Implicit Semantic Data Augmentation for Deep NetworksCode1
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairingCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
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
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
Batch Augmentation with Unimodal Fine-tuning for Multimodal LearningCode0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
Few-Shot Learning for Image Classification of Common FloraCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified