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 21–30 of 308 papers

TitleStatusHype
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations—0
A framework for river connectivity classification using temporal image processing and attention based neural networks—0
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer—0
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
Multi-visual modality micro drone-based structural damage detection—0
deepTerra -- AI Land Classification Made Easy—0
Siamese Networks for Cat Re-Identification: Exploring Neural Models for Cat Instance RecognitionCode0
Image Augmentation Agent for Weakly Supervised Semantic Segmentation—0
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations—0
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image EnhancementCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified