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Created over 6 years ago
Updated over 6 years ago

enricivi

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Implementation of the methods proposed in **Adversarial Training Methods for Semi-Supervised Text Classification** on IMDB dataset (without pre-training)

growing_hierarchical_som

Self-Organizing Map [https://en.wikipedia.org/wiki/Self-organizing_map] is a popular method to perform cluster analysis. SOM shows two main limitations: fixed map size constraints how the data is being mapped and hierarchical relationships are not easily recognizable. Thus Growing Hierarchical SOM has been designed to overcome this issues

Python

super_resolution

Super-resolution is a technique that constructs an high-resolution image from several observed low-resolution images.

MATLAB

image_viewer

A simple image viewer capable of showing EXIF tags

Python

encephalic_vasculature_mapping

This work aims to explore a new approach to model the encephalic vasculature using the formalism of graphs that naturally fit the structure of blood vessels.

Python

SEMI-FALKON

Falkon is one of the most efficient algorithm able to work in a supervised large scale setting. This method is the result of a combination of three simple principles: sub-sampling, preconditioning and iterative solvers. In order to extend FALKON usability we have designed an extension able to work in a semi-supervised scenario.

Python

RWVC-BDD100K

RWVC-BDD100K is a set of image-level annotations on road, weather and visibility condition for a large number of examples from the BDD100K dataset.

enricivi/adversarial_training_methods

enricivi

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