Lagani G
Computer vision Deep neural networks Hebbian learning Machine Learning
Deep learning is becoming more and more popular to extract information from multimedia data for indexing and query processing. In recent contributions, we have explored a biologically inspired strategy for Deep Neural Network (DNN) training, based on the Hebbian principle in neuroscience. We studied hybrid approaches in which unsupervised Hebbian learning was used for a pre-training stage, followed by supervised fine-tuning based on Stochastic Gradient Descent (SGD). The resulting semi-supervised strategy exhibited encouraging results on computer vision datasets, motivating further interest towards applications in the domain of large scale multimedia content based retrieval.
Source: CEUR WORKSHOP PROCEEDINGS, pp. 610-615. Pisa, Italy, 2022
@inproceedings{oai:it.cnr:prodotti:477085, title = {Recent advancements on bio-inspired Hebbian learning for deep neural networks}, author = {Lagani G}, booktitle = {CEUR WORKSHOP PROCEEDINGS, pp. 610-615. Pisa, Italy, 2022}, year = {2022} }