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 126–150 of 308 papers

TitleStatusHype
Few-Shot Learning for Image Classification of Common FloraCode0
Albumentations: fast and flexible image augmentationsCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
LowCLIP: Adapting the CLIP Model Architecture for Low-Resource Languages in Multimodal Image Retrieval TaskCode0
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging—0
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions—0
Camera-based method for the detection of lifted truck axles using convolutional neural networks—0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Exemplar-Free Continual Transformer with Convolutions—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images—0
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified