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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 41–50 of 308 papers

TitleStatusHype
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
Implicit Semantic Data Augmentation for Deep NetworksCode1
An Efficient and Scalable Deep Learning Approach for Road Damage DetectionCode1
Data Augmentation for Scene Text RecognitionCode1
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse InputCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Learning Data Augmentation Strategies for Object DetectionCode1
GANSeg: Learning to Segment by Unsupervised Hierarchical Image GenerationCode1
Show:102550
← PrevPage 5 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified