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 101–125 of 308 papers

TitleStatusHype
Design of an Efficient Distracted Driver Detection System: Deep Learning Approaches—0
Document Layout Analysis with Aesthetic-Guided Image Augmentation—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches—0
DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot—0
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
deepTerra -- AI Land Classification Made Easy—0
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions—0
FitVid: High-Capacity Pixel-Level Video Prediction—0
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach—0
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration—0
Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays—0
Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts—0
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks—0
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review—0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified