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 181–190 of 308 papers

TitleStatusHype
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-trainingCode0
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19—0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Image augmentation improves few-shot classification performance in plant disease recognition—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
Image Augmentation for Satellite Images—0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index—0
Game State Learning via Game Scene Augmentation—0
Augment to Detect Anomalies with Continuous Labelling—0
Exploring Temporally Dynamic Data Augmentation for Video Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified