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 211–220 of 308 papers

TitleStatusHype
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing ImagesCode1
Self-Supervised Pretraining Improves Self-Supervised PretrainingCode1
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
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions—0
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations—0
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
On the Impact of Interpretability Methods in Active Image Augmentation Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified