Paper Detail
Riccardo Casciotti, Francesco De Santis, Alberto Antonietti, Annamaria Mesaros
The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to sound classification. We propose a kernel plasticity approach that selectively modulates network kernels during incremental learning, acting on selected kernels to learn new information and on others to retain previous knowledge. Using the ESC-50 dataset, the proposed method achieves 76.3% overall accuracy over five incremental steps, outperforming a baseline without kernel plasticity (68.7%) and demonstrating significantly greater stability across tasks.
No structured notes yet. Add `summary_sections`, `why_relevant`, `claim_impact`, or `next_action` in `papers.jsonl` to enrich this view.
No ranking explanation is available yet.
No tags.
@article{casciotti2026incremental,
title = {Incremental learning for audio classification with Hebbian Deep Neural Networks},
author = {Riccardo Casciotti and Francesco De Santis and Alberto Antonietti and Annamaria Mesaros},
year = {2026},
abstract = {The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to sound classification. We propose a kernel plasticity approach that selectively modulates network kernels during incremental learning, acting on selected kernels to learn new information and on others to retain previous knowledge. Using the ESC-50 dataset, the proposed method achieves},
url = {https://arxiv.org/abs/2604.18270},
keywords = {eess.AS, cs.LG},
eprint = {2604.18270},
archiveprefix = {arXiv},
}
{}