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

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Showing 101–125 of 308 papers

TitleStatusHype
Data Augmentation via Levy ProcessesCode0
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
Data Augmentation using Random Image Cropping and Patching for Deep CNNsCode0
Domain Generalization with Fourier Transform and Soft ThresholdingCode0
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairingCode0
LowCLIP: Adapting the CLIP Model Architecture for Low-Resource Languages in Multimodal Image Retrieval TaskCode0
Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image AugmentationCode0
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and EstimationCode0
Efficient Method for Categorize Animals in the WildCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-trainingCode0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Compound Figure Separation of Biomedical Images with Side LossCode0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
Reweighting Augmented Samples by Minimizing the Maximal Expected LossCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Albumentations: fast and flexible image augmentationsCode0
Sparse Signal Models for Data Augmentation in Deep Learning ATRCode0
Few-Shot Learning for Image Classification of Common FloraCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified