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

Showing 151160 of 308 papers

TitleStatusHype
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay0
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding0
Semi-supervised object detection based on single-stage detector for thighbone fracture localization0
Semmeldetector: Application of Machine Learning in Commercial Bakeries0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation0
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization0
Slot Based Image Augmentation System for Object Detection0
Spatially Visual Perception for End-to-End Robotic Learning0
Show:102550
← PrevPage 16 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified