18 citations · 18 across the 7 of their papers we have counts for
9 papers
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
Fine-grained classification models are designed to focus on the relevant details necessary to distinguish highly similar classes, particularly when intra-class variance is high and…
A Comparative Study of Transformer and Convolutional Models for Crop Segmentation from Satellite Image Time Series
Mattia Gatti, Ignazio Gallo, Nicola Landro +4
Crop segmentation from satellite image time series (SITS) is a fundamental task for agricultural monitoring and land-use analysis. While convolutional neural networks (CNNs) have b…
Mixing ADAM and SGD: a Combined Optimization Method
Nicola Landro, Ignazio Gallo, Riccardo La Grassa
Optimization methods (optimizers) get special attention for the efficient training of neural networks in the field of deep learning. In literature there are many papers that compar…
R Loss: a Weighted Loss by Multiplicative Factors using Sigmoidal Functions
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
In neural networks, the loss function represents the core of the learning process that leads the optimizer to an approximation of the optimal convergence error. Convolutional neura…
Learn Class Hierarchy using Convolutional Neural Networks
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
A large amount of research on Convolutional Neural Networks has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed a…
Can a powerful neural network be a teacher for a weaker neural network?
Nicola Landro, Ignazio Gallo, Riccardo La Grassa
The transfer learning technique is widely used to learning in one context and applying it to another, i.e. the capacity to apply acquired knowledge and skills to new situations. Bu…
Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate pred…
OCmst: One-class Novelty Detection using Convolutional Neural Network and Minimum Spanning Trees
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and g…
Picture What you Read
Ignazio Gallo, Shah Nawaz, Alessandro Calefati +2
Visualization refers to our ability to create an image in our head based on the text we read or the words we hear. It is one of the many skills that makes reading comprehension pos…