165 citations · 536 across the 24 of their papers we have counts for
29 papers
Retraining-Free Depth Pruning of Vision Transformers: A Cross Domain Study of Layer Redundancy
Alessandro Viespoli, Loris Nanni
Transformer architectures have achieved strong results in natural language processing and computer vision, but their deployment is limited by high computational, memory and storage…
Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation
Marco Stefani, Edoardo Parpaiola, Matteo Bino +5
High-risk applications of deep learning require precise characterization of model decision-making. We conduct a systematic explainability evaluation across Convolutional Neural Net…
A Progressive Design Study of Visual Encoders and Value Estimation for Replay-Free Parallelized Q-Learning
Taha Shieenavaz, Shabnam Zareshahraki, Loris Nanni
Replay-free parallelized Q-learning removes the large experience replay buffers and target networks used by conventional deep Q-learning, but the role of network architecture in th…
Feature transforms for image data augmentation
Loris Nanni, Michelangelo Paci, Sheryl Brahnam +1
A problem with Convolutional Neural Networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many method…
Deep ensembles in bioimage segmentation
Loris Nanni, Daniela Cuza, Alessandra Lumini +2
Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones. During the last few years, a l…
Gated recurrent units and temporal convolutional network for multilabel classification
Loris Nanni, Alessandra Lumini, Alessandro Manfe +3
Multilabel learning tackles the problem of associating a sample with multiple class labels. This work proposes a new ensemble method for managing multilabel classification: the cor…
High performing ensemble of convolutional neural networks for insect pest image detection
Loris Nanni, Alessandro Manfe, Gianluca Maguolo +2
Pest infestation is a major cause of crop damage and lost revenues worldwide. Automatic identification of invasive insects would greatly speedup the identification of pests and exp…
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation
Alessandra Lumini, Loris Nanni, Gianluca Maguolo
Semantic segmentation has a wide array of applications ranging from medical-image analysis, scene understanding, autonomous driving and robotic navigation. This work deals with med…
Comparison of different convolutional neural network activation functions and methods for building ensembles
Loris Nanni, Gianluca Maguolo, Sheryl Brahnam +1
Recently, much attention has been devoted to finding highly efficient and powerful activation functions for CNN layers. Because activation functions inject different nonlinearities…
Deep Features for training Support Vector Machine
Loris Nanni, Stefano Ghidoni, Sheryl Brahnam
Features play a crucial role in computer vision. Initially designed to detect salient elements by means of handcrafted algorithms, features are now often learned by different layer…
Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks
Loris Nanni, Gianluca Maguolo, Alessandra Lumini
Stochastic gradient descent (SGD) is the main approach for training deep networks: it moves towards the optimum of the cost function by iteratively updating the parameters of a mod…
Neural networks for Anatomical Therapeutic Chemical (ATC) classification
Loris Nanni, Alessandra Lumini, Sheryl Brahnam
Motivation: Automatic Anatomical Therapeutic Chemical (ATC) classification is a critical and highly competitive area of research in bioinformatics because of its potential for expe…
Deep learning and hand-crafted features for virus image classification
Loris Nanni, Eugenio De Luca, Marco Ludovico Facin +1
In this work, we present an ensemble of descriptors for the classification of transmission electron microscopy images of viruses. We propose to combine handcrafted and deep learnin…
Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare
Loris Nanni, Alessandra Lumini, Stefano Ghidoni +1
Classification of biological images is an important task with crucial application in many fields, such as cell phenotypes recognition, detection of cell organelles and histopatholo…
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images
Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini +4
COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation…
An Ensemble of Convolutional Neural Networks for Audio Classification
Loris Nanni, Gianluca Maguolo, Sheryl Brahnam +1
In this paper, ensembles of classifiers that exploit several data augmentation techniques and four signal representations for training Convolutional Neural Networks (CNNs) for audi…
The computerization of archaeology: survey on AI techniques
Lorenzo Mantovan, Loris Nanni
This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus…
Phasic dopamine release identification using ensemble of AlexNet
Luca Patarnello, Marco Celin, Loris Nanni
Dopamine (DA) is an organic chemical that influences several parts of behaviour and physical functions. Fast-scan cyclic voltammetry (FSCV) is a technique used for in vivo phasic d…
A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
Gianluca Maguolo, Loris Nanni
In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results ca…
Data augmentation approaches for improving animal audio classification
Loris Nanni, Gianluca Maguolo, Michelangelo Paci
In this paper we present ensembles of classifiers for automated animal audio classification, exploiting different data augmentation techniques for training Convolutional Neural Net…
Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation
Gianluca Maguolo, Michelangelo Paci, Loris Nanni +1
Audio data augmentation is a key step in training deep neural networks for solving audio classification tasks. In this paper, we introduce Audiogmenter, a novel audio data augmenta…
Insect pest image detection and recognition based on bio-inspired methods
Loris Nanni, Gianluca Maguolo, Fabio Pancino
Insect pests recognition is necessary for crop protection in many areas of the world. In this paper we propose an automatic classifier based on the fusion between saliency methods…
Learning morphological operators for skin detection
Alessandra Lumini, Loris Nanni, Alice Codogno +1
In this work we propose a novel post processing approach for skin detectors based on trained morphological operators. The first step, consisting in skin segmentation is performed a…
Deep learning for Plankton and Coral Classification
Alessandra Lumini, Loris Nanni, Gianluca Maguolo
Oceans are the essential lifeblood of the Earth: they provide over 70% of the oxygen and over 97% of the water. Plankton and corals are two of the most fundamental components of oc…