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Sfruttare e Trasferire conoscenza a priori nelle Architetture di Deep Learning

Abstract: In the last decade, Deep Learning has arisen as a hot topic and a disruptive tool in the fields of Machine Learning and Computer Vision. It builds upon a learning paradigm in which data (e.g., videos acquired by surveillance cameras placed on a public road) play a crucial role. By leveraging a great number of data-points, it is possible to fit complex and human-like tasks (e.g., recognizing abnormal actions in a video-stream) with impressive results. However, if data availability represents the source of the greatest strength of Deep Learning techniques, it also reveals the greatest weakness: the development of applications and services is indeed often restrained by such a requirement, as the acquisition and maintenance of a huge amount of data are expensive activities that require expert staff and equipment. However, the design of modern Deep Learning architectures offers several degrees of freedom that can be exploited to mitigate the lack of training data, either partial or complete. The underlying idea is to compensate for it by incorporating a prior knowledge that humans (specifically, those who control and guide the learning process) hold about the domain at hand. Indeed, intrinsic rules and properties extend far beyond training data and can often be identified and imposed on the learner. If we take image classification into account, the success of Convolutional Neural Networks (CNNs) over past solutions (such as Multi-Layered Neural Networks) can be mainly ascribed to such a practice. Indeed, the design principle of its fundamental building block (i.e., the convolution between two 2D-signals) naturally reflect what we knew about natural images: in this regard, the correlations that subsist between neighborhood regions of the image provided so a powerful insight for the development of efficient and effective models as CNNs still prove to be. The ultimate aim of this thesis is the investigation and proposal of novel ways of modeling and injecting prior knowledge in Deep Learning architectures. Importantly, we conduct such a discussion across the board: in fact, it focuses on several data domains (e.g., images, videos, graph-structured data, etc.) and involves different levels of the overall training pipeline. On this latter point, we guide the reader towards this research by means of the following threefold categorization: i) parameter-based approaches, which limit the space of feasible solutions to those regions reflecting geometrical properties of the data; ii) goal-driven approaches, which guide the learning process towards solutions that embody some advantageous properties; iii) data-driven approaches, which exploit data to extract the knowledge to be used to condition the training algorithm. Along with a comprehensive description of both settings and tools involved, we present extensive experimental results and ablation studies that demonstrate the value of the techniques proposed in this research.


Porrello, Angelo "Sfruttare e Trasferire conoscenza a priori nelle Architetture di Deep Learning" 2022

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