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 101–150 of 308 papers

TitleStatusHype
Image Augmentation using Radial Transform for Training Deep Neural Networks—0
Document Layout Analysis with Aesthetic-Guided Image Augmentation—0
Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot—0
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review—0
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions—0
Efficient Augmentation via Data Subsampling—0
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach—0
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
A CNN toolbox for skin cancer classification—0
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks—0
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation—0
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification—0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions—0
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging—0
Image Augmentation for Object Image Classification Based On Combination of PreTrained CNN and SVM—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
Image Augmentation for Satellite Images—0
Image augmentation improves few-shot classification performance in plant disease recognition—0
Image augmentation with conformal mappings for a convolutional neural network—0
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
Attention W-Net: Improved Skip Connections for better Representations—0
A convolutional neural network of low complexity for tumor anomaly detection—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models?—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
Game State Learning via Game Scene Augmentation—0
A Technical Report for VIPriors Image Classification Challenge—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
Image Augmentation Agent for Weakly Supervised Semantic Segmentation—0
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection—0
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes—0
Generative Adversarial U-Net for Domain-free Medical Image Augmentation—0
Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified