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 111120 of 308 papers

TitleStatusHype
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Compound Figure Separation of Biomedical Images with Side LossCode0
Policy Gradient-Driven Noise MaskCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Random Transformation of Image Brightness for Adversarial AttackCode0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
SAC-GAN: Structure-Aware Image CompositionCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified