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 151–175 of 308 papers

TitleStatusHype
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Masked Autoencoders are Robust Data AugmentorsCode1
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?Code0
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes—0
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning—0
Deep PCB To COCO ConvertorCode2
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network—0
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic ReviewCode1
TorMentor: Deterministic dynamic-path, data augmentations with fractalsCode1
Pneumonia Detection in Chest X-Rays using Neural Networks—0
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer SensorsCode0
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints—0
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms—0
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse InputCode1
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network—0
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration—0
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified