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Found 10 papers, 4 papers with code
A Novel Dataset for Financial Education Text Simplification in Spanish
Text simplification, crucial in natural language processing, aims to make texts more comprehensible, particularly for specific groups like visually impaired Spanish speakers, a less-represented language in this field.
Improving Semi-supervised Deep Learning by using Automatic Thresholding to Deal with Out of Distribution Data for COVID-19 Detection using Chest X-ray Images
Frequently, the unlabeled data is more widely available than the labeled data, hence this data is used to improve the level of generalization of a model when the labeled data is scarce.
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.
More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data
In this work, we demonstrate the limits of semantic data set matching.
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.
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures
In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch.
Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification
The layer optimizes the unsharp masking parameters during model training, without any manual intervention.
Ranked #13 on
Scene Text Detection
on ICDAR 2013
