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 111–120 of 308 papers

TitleStatusHype
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review—0
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer—0
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks—0
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
A CNN toolbox for skin cancer classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified