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Learning concepts through the fusion of representations of distinct visual domains with deep learning

Grant number: 18/22191-6
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): March 01, 2019
Effective date (End): July 14, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal researcher:Moacir Antonelli Ponti
Grantee:João Guilherme Madeira Araújo
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated scholarship(s):19/19030-3 - Weight agnostic compositional pattern producing networks and its analysis for learning representations, BE.EP.IC

Abstract

The human cognitive system is able to abstract concepts so that they are related under the same nature. For example, a person alone can be recognized by his or her photo, artistic portrait, caricature, facial composite or even a rough draft. From a computational and pattern recognition point of view, these representations are considered distinct, even though the semantics is the same. The challenge is even greater when there is limited supervision, ie it is a learning problem in a set of unlabeled data or a set with very few labels. The project aims to investigate methods to learn abstract representations from datasets and to relate those representations to other series of distinct domains, but with similar semantics. We will consider feature learning methods, particularly convolutional neural networks, and auto-encoders, and explore architectures capable of achieving a marriage between the learned representations. In addition, each solution will be studied from the point of view of its learning guarantees.

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