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 51–100 of 308 papers

TitleStatusHype
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopyCode1
Learning Data Augmentation Strategies for Object DetectionCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic ReviewCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
Learn to Augment: Joint Data Augmentation and Network Optimization for Text RecognitionCode1
Salient Objects in ClutterCode1
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse InputCode1
Self-Supervised Pretraining Improves Self-Supervised PretrainingCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision TransformerCode1
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image DeformationsCode0
LowCLIP: Adapting the CLIP Model Architecture for Low-Resource Languages in Multimodal Image Retrieval TaskCode0
MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalizationCode0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
Application of Facial Recognition using Convolutional Neural Networks for Entry Access ControlCode0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
Learning to Compose Domain-Specific Transformations for Data AugmentationCode0
Long Tail Image Generation Through Feature Space Augmentation and Iterated LearningCode0
Batch Augmentation with Unimodal Fine-tuning for Multimodal LearningCode0
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and EstimationCode0
Language-Driven Dual Style Mixing for Single-Domain Generalized Object DetectionCode0
Learning deep illumination-robust features from multispectral filter array imagesCode0
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
Adversarial Augmentation for Enhancing Classification of Mammography ImagesCode0
Isometric Transformations for Image Augmentation in Mueller Matrix PolarimetryCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification TasksCode0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
An Empirical Study of Validating Synthetic Data for Text-Based Person RetrievalCode0
Improving Fairness using Vision-Language Driven Image AugmentationCode0
Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairingCode0
DenseNet Models for Tiny ImageNet ClassificationCode0
Improved Mixed-Example Data AugmentationCode0
Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image AugmentationCode0
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
Augmentor: An Image Augmentation Library for Machine LearningCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image AugmentationCode0
Population Based Augmentation: Efficient Learning of Augmentation Policy SchedulesCode0
Data Augmentation via Levy ProcessesCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Data Augmentation using Random Image Cropping and Patching for Deep CNNsCode0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer SensorsCode0
Few-Shot Learning for Image Classification of Common FloraCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified