Retraining SNN conversions: CNN to SNN for audio classification tasks - Chang, John Rong Qi (Nanyang Technological University, 2023)
Efficient yet powerful models are in high demand for its portability and affordability. Amongst other methods such as model-pruning, is limiting neural network operations to sparse event-driven spikes: Spiking Neural Networks (SNNs) aims to unravel a new direction in machine learning research. A significant amount of SNN literature straddles upon mature works of artificial neural networks (ANNs) by migrating its architecture and parameters into SNNs, optimizing the migration to retain as much performance as possible. We spearhead a novel approach: the architecture is migrated and retrained from scratch. We hypothesize that this new direction will unravel concepts that currently bottlenecks improvements in the field of SNN conversions. Furthermore, alike Transfer Learning, inspire future efforts of fine-tuning a well converted model through training.