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 201–210 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified