For a long time, biology and neuroscience fields have been a great source of inspiration for
computer scientists, towards the development of Artificial Intelligence (AI) technologies. This …

Recently emerged technologies based on Deep Learning (DL) achieved outstanding results
on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter …

Recent work on sample efficient training of Deep Neural Networks (DNNs) proposed a semi-
supervised methodology based on biologically inspired Hebbian learning, combined with …

Due to industrial demands to handle increasing amounts of training data, lower the cost of
computing one model at a time, and lessen the ecological effects of intensive computing …

We propose a novel two-stage semi-supervised learning approach for training
downsampling-upsampling semantic segmentation architectures. The first stage does not …

Given the unprecedented growth of deep learning applications, training acceleration is
becoming a subject of strong academic interest. Hebbian learning as a training strategy …

In the last decade, approaches in feature extraction for content-based multimedia retrieval
exploited neural feature representations to describe complex data types such as images. In …

In this short paper, we report the activities of the Artificial Intelligence for Media and
Humanities (AIMH) laboratory of the ISTI-CNR related to Sustainable AI. In particular, we …

The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to
investigate and advance the state of the art in the Artificial Intelligence field, specifically …

The research presented in this paper advances the integration of Hebbian learning into
Convolutional Neural Networks (CNNs) for image processing, systematically exploring …