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 41–50 of 308 papers

TitleStatusHype
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
An Efficient and Scalable Deep Learning Approach for Road Damage DetectionCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Improving the Transferability of Adversarial Samples by Path-Augmented MethodCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Learning Data Augmentation Strategies for Object DetectionCode1
FitVid: Overfitting in Pixel-Level Video PredictionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified