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 231–240 of 308 papers

TitleStatusHype
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Survey: Image Mixing and Deleting for Data AugmentationCode0
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation—0
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation—0
Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture—0
Few-Shot Learning for Image Classification of Common FloraCode0
Neural Networks for Semantic Gaze Analysis in XR Settings—0
Hierarchical Attention-based Age Estimation and Bias Estimation—0
Reweighting Augmented Samples by Minimizing the Maximal Expected LossCode0
Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified