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
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions—0
Semmeldetector: Application of Machine Learning in Commercial Bakeries—0
How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models?—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies—0
Long Tail Image Generation Through Feature Space Augmentation and Iterated LearningCode0
Policy Gradient-Driven Noise MaskCode0
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
UniMERNet: A Universal Network for Real-World Mathematical Expression RecognitionCode3
XoFTR: Cross-modal Feature Matching TransformerCode2
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
DiffClass: Diffusion-Based Class Incremental Learning—0
Outline-Guided Object Inpainting with Diffusion Models—0
Fiducial Focus Augmentation for Facial Landmark Detection—0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks—0
A Survey on Data Augmentation in Large Model EraCode2
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN—0
Leveraging Habitat Information for Fine-grained Bird Identification—0
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image DeformationsCode0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining—0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions—0
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning—0
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images—0
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques—0
Improving Fairness using Vision-Language Driven Image AugmentationCode0
DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot—0
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation—0
Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
Selective Volume Mixup for Video Action RecognitionCode0
Domain Generalization with Fourier Transform and Soft ThresholdingCode0
Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images—0
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies—0
MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalizationCode0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation—0
Handwritten image augmentation—0
Exemplar-Free Continual Transformer with Convolutions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified