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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 308 papers

TitleStatusHype
Zero-Shot Learning by Harnessing Adversarial SamplesCode0
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
RaViTT: Random Vision Transformer Tokens—0
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization—0
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Realistic Data Enrichment for Robust Image Segmentation in Histopathology—0
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
Progressive Random Convolutions for Single Domain Generalization—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image SegmentationCode0
Towards Composable Distributions of Latent Space Augmentations—0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
LMSeg: Language-guided Multi-dataset Segmentation—0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification—0
A convolutional neural network of low complexity for tumor anomaly detection—0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks—0
Design of Arabic Sign Language Recognition Model—0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training—0
Diagnosis of COVID-19 based on Chest Radiography—0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
Image augmentation with conformal mappings for a convolutional neural network—0
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications—0
Design of an Efficient Distracted Driver Detection System: Deep Learning Approaches—0
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images—0
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments—0
Semi-supervised object detection based on single-stage detector for thighbone fracture localization—0
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-trainingCode0
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19—0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Image augmentation improves few-shot classification performance in plant disease recognition—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
Image Augmentation for Satellite Images—0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index—0
Game State Learning via Game Scene Augmentation—0
Augment to Detect Anomalies with Continuous Labelling—0
Exploring Temporally Dynamic Data Augmentation for Video Recognition—0
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
A machine-generated catalogue of Charon's craters and implications for the Kuiper beltCode0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?Code0
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes—0
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified