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 251260 of 308 papers

TitleStatusHype
An Empirical Study of Validating Synthetic Data for Text-Based Person RetrievalCode0
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-trainingCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency LearningCode0
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairingCode0
Improved Mixed-Example Data AugmentationCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Policy Gradient-Driven Noise MaskCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified