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 131–140 of 308 papers

TitleStatusHype
Image augmentation with conformal mappings for a convolutional neural network—0
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric LearningCode1
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
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial ImagesCode1
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified