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 171–180 of 308 papers

TitleStatusHype
LMSeg: Language-guided Multi-dataset Segmentation—0
Medical Image Generation using Generative Adversarial Networks—0
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method—0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training—0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining—0
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology—0
Multi-visual modality micro drone-based structural damage detection—0
Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks—0
Neural Networks for Semantic Gaze Analysis in XR Settings—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified