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 161–170 of 308 papers

TitleStatusHype
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy—0
Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data—0
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Learning More with Less: GAN-based Medical Image Augmentation—0
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge—0
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints—0
Leveraging Habitat Information for Fine-grained Bird Identification—0
Show:102550
← PrevPage 17 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified