A kernel plasticity approach in Hebbian DNNs for incremental sound classification achieves 76.3% accuracy over five steps on ESC-50, outperforming the 68.7% baseline without plasticity.
The incremen- tal learning process is controlled by a number of hyperparameters selected using the validation set
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Incremental learning for audio classification with Hebbian Deep Neural Networks
A kernel plasticity approach in Hebbian DNNs for incremental sound classification achieves 76.3% accuracy over five steps on ESC-50, outperforming the 68.7% baseline without plasticity.