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 121–130 of 308 papers

TitleStatusHype
LMSeg: Language-guided Multi-dataset Segmentation—0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification—0
A convolutional neural network of low complexity for tumor anomaly detection—0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks—0
Design of Arabic Sign Language Recognition Model—0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training—0
Diagnosis of COVID-19 based on Chest Radiography—0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified