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 201–225 of 308 papers

TitleStatusHype
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network—0
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
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
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
SAC-GAN: Structure-Aware Image CompositionCode0
Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image AugmentationCode0
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video—0
A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce—0
Document Layout Analysis with Aesthetic-Guided Image Augmentation—0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels—0
Attention W-Net: Improved Skip Connections for better Representations—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
A Technical Report for ICCV 2021 VIPriors Re-identification Challenge—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
FitVid: High-Capacity Pixel-Level Video Prediction—0
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge—0
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image CaptioningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified