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Found 6 papers, 2 papers with code
mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms
Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios.
Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey
In a semi-supervised setting, unlabelled data is used to improve the levels of accuracy and generalization of a model with small labelled datasets.
Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities
This results in a distribution mismatch between the unlabelled and labelled datasets.
A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica
The use of two popular and publicly available datasets (INbreast and CBIS-DDSM) as source data, to train and test the models on the novel target dataset, is evaluated.
Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images
To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model.
Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images
In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch using a very limited number of labelled observations and highly imbalanced labelled dataset.
