A Forward-Forward training framework that freezes spiking layers as black-box encoders and allocates channels by inter-class difficulty achieves 99.58% on MNIST, 92.13% on Fashion-MNIST, and 75.64% on CIFAR-10, the best among FF-based SNN methods.
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FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks
A Forward-Forward training framework that freezes spiking layers as black-box encoders and allocates channels by inter-class difficulty achieves 99.58% on MNIST, 92.13% on Fashion-MNIST, and 75.64% on CIFAR-10, the best among FF-based SNN methods.