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2020 Report Open Access OPEN

AIMH research activities 2020
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Carrara F., Casarosa V., Ciampi L., Concordia C., Corbara S., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Thanos C., Trupiano L., Vadicamo L., Vairo C.
Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2019 Conference article Open Access OPEN

Hebbian learning meets deep convolutional neural networks
Amato G., Carrara F., Falchi F., Gennaro C., Lagani G.
Neural networks are said to be biologically inspired since they mimic the behavior of real neurons. However, several processes in state-of-the-art neural networks, including Deep Convolutional Neural Networks (DCNN), are far from the ones found in animal brains. One relevant difference is the training process. In state-of-the-art artificial neural networks, the training process is based on backpropagation and Stochastic Gradient Descent (SGD) optimization. However, studies in neuroscience strongly suggest that this kind of processes does not occur in the biological brain. Rather, learning methods based on Spike-Timing-Dependent Plasticity (STDP) or the Hebbian learning rule seem to be more plausible, according to neuroscientists. In this paper, we investigate the use of the Hebbian learning rule when training Deep Neural Networks for image classification by proposing a novel weight update rule for shared kernels in DCNNs. We perform experiments using the CIFAR-10 dataset in which we employ Hebbian learning, along with SGD, to train parts of the model or whole networks for the task of image classification, and we discuss their performance thoroughly considering both effectiveness and efficiency aspects.Source: Image Analysis and Processing - ICIAP 2019, pp. 324–334, Trento, Italia, 9/9/2019, 13/9/2019
DOI: 10.1007/978-3-030-30642-7_29
Project(s): AI4EU via OpenAIRE

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2019 Report Open Access OPEN

AIMIR 2019 Research Activities
Amato G., Bolettieri P., Carrara F., Ciampi L., Di Benedetto M., Debole F., Falchi F., Gennaro C., Lagani G., Massoli F. V., Messina N., Rabitti F., Savino P., Vadicamo L., Vairo C.
Multimedia Information Retrieval (AIMIR) research group is part of the NeMIS laboratory of the Information Science and Technologies Institute "A. Faedo" (ISTI) of the Italian National Research Council (CNR). The AIMIR group has a long experience in topics related to: Artificial Intelligence, Multimedia Information Retrieval, Computer Vision and Similarity search on a large scale. We aim at investigating the use of Artificial Intelligence and Deep Learning, for Multimedia Information Retrieval, addressing both effectiveness and efficiency. Multimedia information retrieval techniques should be able to provide users with pertinent results, fast, on huge amount of multimedia data. Application areas of our research results range from cultural heritage to smart tourism, from security to smart cities, from mobile visual search to augmented reality. This report summarize the 2019 activities of the research group.Source: AIMIR Annual Report, 2019

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