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|Authors: ||Calefati, Alessandro|
|Internal Tutor: ||GALLO, IGNAZIO|
|Title: ||Discriminative feature learning for multimodal classification|
|Abstract: ||The purpose of this thesis is to tackle two related topics: multimodal classification and objective functions to improve the discriminative power of features.
First, I worked on image and text classification tasks and performed many experiments to show the effectiveness of different approaches available in literature.
Then, I introduced a novel methodology which can classify multimodal documents using singlemodal classifiers merging textual and visual information into images and a novel loss function to improve separability between samples of a dataset.
Results show that exploiting multimodal data increases performances on classification tasks rather than using traditional single-modality methods.
Moreover the introduced GIT loss function is able to enhance the discriminative power of features, lowering intra-class distance and raising inter-class distance between samples of a multiclass dataset.|
|Keywords: ||Multimodal clasification, dataset separability, deep features, machine learning, deep learning, image classification, text classification|
|Subject MIUR : ||INF/01 INFORMATICA|
|Issue Date: ||2019|
|Doctoral course: ||Informatica e matematica del calcolo|
|Academic cycle: ||32|
|Publisher: ||Università degli Studi dell'Insubria|
|Citation: ||Calefati, A.Discriminative feature learning for multimodal classification (Doctoral Thesis, Università degli Studi dell'Insubria, 2019).|
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|PhD_Thesis_CalefatiAlessandro_completa.pdf||testo completo tesi||16,01 MB||Adobe PDF||View/Open
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