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
FitVid: Overfitting in Pixel-Level Video PredictionCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
An Efficient and Scalable Deep Learning Approach for Road Damage DetectionCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse InputCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric LearningCode1
Data Augmentation for Scene Text RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified