Handwriting styles: benchmarks and evaluation metrics

Evaluating the style of handwriting generation is a challenging problem, since it is not well defined. It is a key component in order to develop in developing systems with more personalized experiences with humans. In this paper, we propose baseline benchmarks, in order to set anchors to estimate the relative quality of different handwriting style methods. This will be done using deep learning techniques, which have shown remarkable results in different machine learning tasks, learning classification, regression, and most relevant to our work, generating temporal sequences. We discuss the challenges associated with evaluating our methods, which is related to evaluation of generative models in general. We then propose evaluation metrics, which we find relevant to this problem, and we discuss how we evaluate the evaluation metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge, there is no work done before in generating handwriting (either in terms of methodology or the performance metrics), our in exploring styles using this dataset.Comment: Submitted to IEEE International Workshop on Deep and Transfer Learning (DTL 2018

Style Transfer and Extraction for the Handwritten Letters Using Deep Learning

How can we learn, transfer and extract handwriting styles using deep neural networks? This paper explores these questions using a deep conditioned autoencoder on the IRON-OFF handwriting data-set. We perform three experiments that systematically explore the quality of our style extraction procedure. First, We compare our model to handwriting benchmarks using multidimensional performance metrics. Second, we explore the quality of style transfer, i.e. how the model performs on new, unseen writers. In both experiments, we improve the metrics of state of the art methods by a large margin. Lastly, we analyze the latent space of our model, and we see that it separates consistently writing styles.Comment: Accepted in ICAART 201

Planification distribuée par fusions incrémentales de graphes

National audienceDans cet article, nous proposons un modèle générique et original pour la synthèse distribuée de plans par un groupe d'agents, appelé " planification distribuée par fusions incrémentales de graphes ". Ce modèle unifie de manière élégante les différentes phases de la planification distribuée au sein d'un même processus. Le modèle s'appuie sur les graphes de planification, utilisé en planification mono-agent, pour permettre aux agents de raisonner et sur une technique de satisfaction de contraintes pour l'extraction et la coordination des plans individuels. L'idée forte du modèle consiste à intégrer au plus tôt, \ie au sein du processus local de planification, la phase de coordination. L'unification de ces phases permet ainsi aux agents de limiter les interactions négatives entre leurs plans individuels, mais aussi, de prendre en compte leurs interactions positives, \ie d'aide ou d'assistance, lors de l'extraction de leurs plans individuels

Agent-based semantic composition of Web services using distributed description logics

International audienceAn important research challenge consists in composing web services in an automatic and distributed manner on a large scale. Indeed, most queries can not be satisfiable by one service and must be processed by composing several services. Each web service is often written by different designers and is described using the terms of their own ontology. Therefore, the composition process needs to deal with a variety of heterogeneous ontologies. In order to tackle this challenge, we propose an approach using Distributed Description Logics (DDL) to achieve the semantic composition of web services. DDL allows one to make semantic connections between ontologies and thus web services, as well as to reason to get a semantic composition of web services

Approche multi-agent pour l'exploration coordonnée d'un environnement labyrinthique 2D

National audienceCet article présente une approche multi-agent résolvant le problème de "poursuite-évasion" pour un ou plusieurs robots mobiles coopératifs possédant une vision omnidirectionnelle. Cette approche possède l'originalité de mettre en oeuvre une coopération réelle entre les agents fondée sur un partage de connaissances. Un algorithme complet pour une patrouille de robots reposant sur une décomposition de l'environnement en points critiques est présenté. Nous abordons son application dans le cadre de l'exploration coordonnée d'un environnement inconnu et nous présentons quelques résultats de simulation