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
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Document Layout Analysis with Aesthetic-Guided Image Augmentation—0
Exemplar-Free Continual Transformer with Convolutions—0
Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays—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
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis—0
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions—0
Efficient Augmentation via Data Subsampling—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified