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Found 7 papers, 5 papers with code
Semi-Supervised Machine Learning: a Homological Approach
In this paper we describe the mathematical foundations of a new approach to semi-supervised Machine Learning.
Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context
By combining semi-supervised learning methods with compact networks, it is possible to obtain a similar performance to standard size networks.
A Topological Approach for Semi-Supervised Learning
In the former, we have used a homological approach that consists in studying the persistence diagrams associated with the data using the Bottleneck and Wasserstein distances.
Neural Style Transfer and Unpaired Image-to-Image Translation to deal with the Domain Shift Problem on Spheroid Segmentation
In this work, we address this challenge by studying both neural style transfer algorithms and unpaired image-to-image translation methods in the context of the segmentation of tumour spheroids.
Text Classification Models for Form Entity Linking
Forms are a widespread type of template-based document used in a great variety of fields including, among others, administration, medicine, finance, or insurance.
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques
In this work, we present FrImCla, an open-source and free tool that simplifies the construction of robust models for image classification from a dataset of images, and only using the computer CPU.
The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images
A correct localisation of tables in a document is instrumental for determining their structure and extracting their contents; therefore, table detection is a key step in table understanding.
