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 101–110 of 308 papers

TitleStatusHype
Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data SynthesisCode0
LowCLIP: Adapting the CLIP Model Architecture for Low-Resource Languages in Multimodal Image Retrieval TaskCode0
Enhancing Autonomous Vehicle Perception in Adverse Weather through Image Augmentation during Semantic Segmentation TrainingCode0
Learning deep illumination-robust features from multispectral filter array imagesCode0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified