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 201–225 of 308 papers

TitleStatusHype
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Augment to Detect Anomalies with Continuous Labelling—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches—0
Automatic phantom test pattern classification through transfer learning with deep neural networks—0
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions—0
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies—0
BGM: Background Mixup for X-ray Prohibited Items Detection—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Camera-based method for the detection of lifted truck axles using convolutional neural networks—0
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN—0
CIMON: Towards High-quality Hash Codes—0
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations—0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified