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
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications—0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
Semi-supervised object detection based on single-stage detector for thighbone fracture localization—0
Semmeldetector: Application of Machine Learning in Commercial Bakeries—0
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation—0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation—0
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization—0
Slot Based Image Augmentation System for Object Detection—0
Spatially Visual Perception for End-to-End Robotic Learning—0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection—0
Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge—0
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks—0
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations—0
Temperate Fish Detection and Classification: a Deep Learning based Approach—0
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network—0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Towards Composable Distributions of Latent Space Augmentations—0
Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning—0
Towards Performance Improvement in Indian Sign Language Recognition—0
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms—0
USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE—0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified