Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models

The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover latent thematic structure in discrete data. Despite the success of these models on textual data, they have not generalized as well to image data, in part because of the spatial and temporal structure that may exist in an image stream. We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By using this hybrid framework, we overcome the fundamental dependency of traditional topic models on rigidly hand-coded data representations, while simultaneously encoding spatial dependency in our topics without adding model complexity. We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. Our experiments on a seafloor dataset collected by a marine robot show that the proposed hybrid framework outperforms current state-of-the-art approaches on the task of unsupervised seafloor terrain characterization.Comment: 8 page

Object learning through active exploration

International audienceThis paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts

A Deep Hierarchical Architeture for learning with few data

Orientador: Esther Luna ColombiniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O aprendizado de conceitos nos permite construir sistemas capazes de classificar objetos, eventos ou ideias baseado no fato de que cada um possui um conjunto de características relevantes que os diferencia e que destaca suas similaridades. Atualmente existem diversas técnicas aplicadas ao aprendizado de conceitos. Contudo, em sua maioria, elas dependem de uma quantidade muito grande de dados para se obter um bom resultado, o que nem sempre é possível. Além disso, em geral, o conjunto de conceitos a ser aprendido precisa ser conhecido a priori, ou seja, precisa estar rotulado. Neste contexto, a principal vantagem das técnicas não-supervisionadas é permitir a extração de informações relevantes dos objetos e seu agrupamento para uso posterior sem conhecimento a priori. Neste trabalho propomos uma arquitetura não-supervisionada que utiliza uma Máquina de Boltzmann Profunda (DBM) e um Processo Dirichlet Hierarquico (HDP) para aprender a separar classes de objetos e observar o compartilhamento de caracterísiticas entre as mesmas. Para avaliar a possibilidade de aprender com poucos dados sobre a arquitetura proposta, utilizamos técnicas de aumento de dados e saliência associadas à rede profunda. Resultados experimentais realizados com imagens mostram que a acurácia do sistema com o protocolo proposto pode ser equivalente ou até superior aquela obtida por um sistema com quatro vezes a quantidade de exemplos apresentados em fase de treinamentoAbstract: Concept learning allows us to build systems that can classify objects, events, or ideas based on the fact that each has a set of relevant characteristics that differentiate them and highlight their similarities. There are currently several techniques applied to concept learning. However, for the most part, they rely on too much data for a good result, which is not always possible. Besides, in general, the set of concepts to be learned needs to be known a priori, ie, it must be labeled. In this context, the main advantage of unsupervised techniques is that it allows the extraction of relevant information from objects and their grouping for later use without prior knowledge. In this work, we propose an unsupervised architecture that uses a Deep Boltzmann Machine (DBM) and a Hierarchical Dirichlet Process (HDP) to learn how to separate object classes and observe how they share features. To evaluate the possibility of learning with few data on the proposed architecture, we used data augmentation and salience techniques. Experimental results with images show that the accuracy of the system with the proposed protocol can be equivalent to or even higher than that obtained by a system with four times the amount of examples presented in the training phaseMestradoCiência da ComputaçãoMestra em Ciência da Computação134576/2017-9CNP

Deep Learning For Sequential Pattern Recognition

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM)In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with the conventional algorithms for optimization and training make them efficient for variety of applications in signal processing and pattern recognition. This thesis explores these novel techniques and their related algorithms. It addresses and compares different attributes of these methods, sketches in their possible advantages and disadvantages

Diversidad en aprendizaje profundo por auto-codificación

El diseño de aprendices profundos generales se ha mantenido como reto durante décadas. En el siglo actual se está produciendo la aparición de varios nuevos –y eficaces– procedimientos para ello. Esos procedimientos incluyen los métodos representacionales, que merecen especial atención porque no solo permiten construir máquinas potentes, sino que también extraen relevantes rasgos de alto nivel de las observaciones. Los auto-codificadores expansivos reductores de ruido son (elementos de) una de las familias de máquinas representacionales profundas. Por otra parte, los conjuntos son una alternativa sólidamente establecida para conseguir soluciones con altas prestaciones para problemas empíricos –basados en muestras– de inferencia. Se valen de la introducción de diversidad en un grupo de aprendices. Obviamente, este es un principio que también puede aplicarse a redes neuronales profundas; pero, sorprendentemente, hay muy pocos estudios que exploran esta posibilidad. En esta disertación doctoral se investiga si las técnicas convencionales de diversificación –incluyendo la binarización en el caso de bases de datos multiclase– permiten mejorar las prestaciones de clasificadores basados en auto-codificadores expansivos con reducción de ruido. Se usan tanto “Bagging” como “Switching”, junto con esquemas de binarización uno-contra-uno y de códigos de salida correctores de errores, sobre dos tipos básicos de arquitecturas: T, que tiene una unidad de auto-codificación común, y G, que también diversifica ese elemento representacional. Los resultados experimentales confirman que –si se incluye la binarización– la combinación de diversidad y profundidad conduce a mejores prestaciones, especialmente con las arquitecturas T. Para completar la exploración sobre posibles mejoras, se analiza también la aplicación de formas flexibles de pre-énfasis. Tales formas proporcionan por sí solas mejoras de prestaciones, pero las mejoras son muy importantes cuando el pre-énfasis se combina con la diversificación, en especial si se emplean diferentes parámetros de pre-énfasis a diferentes dicotomías en los problemas multiclase. Una distorsión elástica convencional permite alcanzar resultados récord. Estos resultados no son tan solo relevantes “per se”, sino que abren una vía de prometedoras líneas de investigación, las cuales se exponen en el capítulo final de esta tesis.Designing general deep learners has remained as a challenge along decades. The present century sees the emergence of several new effective procedures for it. Among them, representational methods merit particular attention, because they not only serve to build powerful machines, but also extract relevant high-level features of the observations. Expansive denoising auto-encoders are (elements of) one of such representational deep machine families. On the other hand, ensembles are a well established alternative to get high performance solutions for empirical –sample based– inference problems. They are principled on introducing diversity in a number of different learners. Obviously, this is a principle which can also be applied to deep neural networks, but, surprisingly, there are very few studies exploring this possibility. In this doctoral dissertation, we investigate if conventional diversification techniques –including binarization for multiclass databases– further improve the performance of expansive denoising auto-encoder based classifiers. Both “Bagging” and “Switching” are used, as well as one-versus-one and error-correcting-output-code binarization schemes, with two basic types of architectures: T, which has a common auto-encoding unit, and G, which also diversifies that representational element. The experimental results confirm that –if binarization is included– combining diversity and depth offers significant performance advantages, specially with T architectures. To complete the exploration on improving denoising auto-encoding based classifiers, the application of flexible enough pre-emphasis functions is also analyzed. Using this kind of pre-emphasis provides performance advantages by itself, but the advantages are very important when pre-emphasis is combined with diversification, specially if different emphasis parameters are applied to different dichotomies in multiclass problems. A conventional elastic distortion allows record results. These results are not only relevant by themselves, but they open a series of promising research avenues, that are presented in the final chapter of this thesis.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Antonio Artés Rodríguez.- Secretario: Sancho Salcedo Sanz.- Vocal: Pedro Antonio Gutiérrez Peñ