18 citations · 35 across the 14 of their papers we have counts for
18 papers
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…
Cross-modal Speaker Verification and Recognition: A Multilingual Perspective
Muhammad Saad Saeed, Shah Nawaz, Pietro Morerio +4
Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we int…
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…
Deep Latent Space Learning for Cross-modal Mapping of Audio and Visual Signals
Shah Nawaz, Muhammad Kamran Janjua, Ignazio Gallo +2
We propose a novel deep training algorithm for joint representation of audio and visual information which consists of a single stream network (SSNet) coupled with a novel loss func…
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…
A Classification Methodology based on Subspace Graphs Learning
Riccardo La Grassa, Ignazio Gallo, Alessandro Calefati +1
In this paper, we propose a design methodology for one-class classifiers using an ensemble-of-classifiers approach. The objective is to select the best structures created during th…
Do Cross Modal Systems Leverage Semantic Relationships?
Shah Nawaz, Muhammad Kamran Janjua, Ignazio Gallo +3
Current cross-modal retrieval systems are evaluated using R@K measure which does not leverage semantic relationships rather strictly follows the manually marked image text query pa…
Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees
Riccardo La Grassa, Ignazio Gallo, Alessandro Calefati +1
One-class classifiers are trained with target class only samples. Intuitively, their conservative modelling of the class description may benefit classical classification tasks wher…
Aiding Intra-Text Representations with Visual Context for Multimodal Named Entity Recognition
Omer Arshad, Ignazio Gallo, Shah Nawaz +1
With massive explosion of social media such as Twitter and Instagram, people daily share billions of multimedia posts, containing images and text. Typically, text in these posts is…
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions
Muhammad Kamran Janjua, Shah Nawaz, Alessandro Calefati +1
Majority of the current dimensionality reduction or retrieval techniques rely on embedding the learned feature representations onto a computable metric space. Once the learned feat…
Image and Encoded Text Fusion for Multi-Modal Classification
Ignazio Gallo, Alessandro Calefati, Shah Nawaz +1
Multi-modal approaches employ data from multiple input streams such as textual and visual domains. Deep neural networks have been successfully employed for these approaches. In thi…
Seeing Colors: Learning Semantic Text Encoding for Classification
Shah Nawaz, Alessandro Calefati, Muhammad Kamran Janjua +1
The question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents? To answer this question we…
Git Loss for Deep Face Recognition
Alessandro Calefati, Muhammad Kamran Janjua, Shah Nawaz +1
Convolutional Neural Networks (CNNs) have been widely used in computer vision tasks, such as face recognition and verification, and have achieved state-of-the-art results due to th…
Revisiting Cross Modal Retrieval
Shah Nawaz, Muhammad Kamran Janjua, Alessandro Calefati +1
This paper proposes a cross-modal retrieval system that leverages on image and text encoding. Most multimodal architectures employ separate networks for each modality to capture th…