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 51–75 of 308 papers

TitleStatusHype
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing ImagesCode1
TorMentor: Deterministic dynamic-path, data augmentations with fractalsCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
Unsupervised Data Augmentation for Consistency TrainingCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-CaseCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric LearningCode1
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial ImagesCode1
Camera-based method for the detection of lifted truck axles using convolutional neural networks—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening—0
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
A framework for river connectivity classification using temporal image processing and attention based neural networks—0
BGM: Background Mixup for X-ray Prohibited Items Detection—0
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies—0
Diagnosis of COVID-19 based on Chest Radiography—0
DiffClass: Diffusion-Based Class Incremental Learning—0
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified