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Found 4 papers, 1 papers with code
Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging
Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets, e. g., when the sets are produced by different hardware.
Data Augmentation with Manifold Barycenters
Oftentimes, these methods either fail to produce enough new data or expand the dataset beyond the original knowledge domain.
Global Adaptive Filtering Layer for Computer Vision
We devise a universal adaptive neural layer to "learn" optimal frequency filter for each image together with the weights of the base neural network that performs some computer vision task.
BRULÈ: Barycenter-Regularized Unsupervised Landmark Extraction
Unsupervised retrieval of image features is vital for many computer vision tasks where the annotation is missing or scarce.
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