896 research outputs found
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
Every Smile is Unique: Landmark-Guided Diverse Smile Generation
Each smile is unique: one person surely smiles in different ways (e.g.,
closing/opening the eyes or mouth). Given one input image of a neutral face,
can we generate multiple smile videos with distinctive characteristics? To
tackle this one-to-many video generation problem, we propose a novel deep
learning architecture named Conditional Multi-Mode Network (CMM-Net). To better
encode the dynamics of facial expressions, CMM-Net explicitly exploits facial
landmarks for generating smile sequences. Specifically, a variational
auto-encoder is used to learn a facial landmark embedding. This single
embedding is then exploited by a conditional recurrent network which generates
a landmark embedding sequence conditioned on a specific expression (e.g.,
spontaneous smile). Next, the generated landmark embeddings are fed into a
multi-mode recurrent landmark generator, producing a set of landmark sequences
still associated to the given smile class but clearly distinct from each other.
Finally, these landmark sequences are translated into face videos. Our
experimental results demonstrate the effectiveness of our CMM-Net in generating
realistic videos of multiple smile expressions.Comment: Accepted as a poster in Conference on Computer Vision and Pattern
Recognition (CVPR), 201
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