Open ghost opened 1 month ago
Hi, sorry for the late reply.
(1) The alpha_init is to confirm the sparsity of SNNs. It may result in a high spike firing rate if you drop it.
(2) Yes, it is a general spike encoding method.
(3) You can refer to Section "4.2. Spike Frequency Encoding" in the paper, this is the implementation of how to calculate the alpha.
Appreciate your work!
I have some questions about the code in spiking.py
(1) Is grad_scale necessary? Because I found that if I eliminated grad_scale, my own model converged faster (without using AlphaInit).
(2) Is the ElasticBiSpiking method suitable for CV tasks such as classification and object detection?
(3) Should I always use ElasticBiSpiking with AlphaInit? Judging from your paper, I think the answer is no. If I want to use ElasticBiSpiking method without AlphaInit , should I remove grad_scale ? Because I guess grad_scale makes the gradient too small.
Thanks again for your work! This is my first post on GitHub.