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Found 5 papers, 2 papers with code
Self-mentoring: a new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation
Conclusions: Experimental results show that very significant accuracy increments are obtained by the proposed pipeline, leading to the conclusion that the self-supervision mechanism introduced in this paper has the potential to replace human annotations.
State space partitioning based on constrained spectral clustering for block particle filtering
The particle filter (PF) is a powerful inference tool widely used to estimate the filtering distribution in non-linear and/or non-Gaussian problems.
SODA: Self-organizing data augmentation in deep neural networks -- Application to biomedical image segmentation tasks
In practice, data augmentation is assigned a predefined budget in terms of newly created samples per epoch.
SPOCC: Scalable POssibilistic Classifier Combination -- toward robust aggregation of classifiers
We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task.
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
We examine a network of learners which address the same classification task but must learn from different data sets.
