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 176–200 of 308 papers

TitleStatusHype
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology—0
Multi-visual modality micro drone-based structural damage detection—0
Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks—0
Neural Networks for Semantic Gaze Analysis in XR Settings—0
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning—0
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation—0
On the Impact of Interpretability Methods in Active Image Augmentation Method—0
Outline-Guided Object Inpainting with Diffusion Models—0
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation—0
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19—0
Pneumonia Detection in Chest X-Rays using Neural Networks—0
Polarimetric image augmentation—0
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation—0
Progressive Random Convolutions for Single Domain Generalization—0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
RaViTT: Random Vision Transformer Tokens—0
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments—0
Realistic Data Enrichment for Robust Image Segmentation in Histopathology—0
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
rQdia: Regularizing Q-Value Distributions With Image Augmentation—0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions—0
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified