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 51–60 of 308 papers

TitleStatusHype
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing ImagesCode1
Learning Data Augmentation Strategies for Object DetectionCode1
Masked Autoencoders are Robust Data AugmentorsCode1
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision TransformerCode1
Adversarial Policy Gradient for Deep Learning Image AugmentationCode1
Salient Objects in ClutterCode1
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial ImagesCode1
Self-Supervised Pretraining Improves Self-Supervised PretrainingCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
FitVid: Overfitting in Pixel-Level Video PredictionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified