ESVAE: An Efficient Spiking Variational Autoencoder with
  Reparameterizable Poisson Spiking Sampling
Qiugang Zhan Xiurui Xie Guisong Liu Malu Zhang
In recent years, studies on image generation models of spiking neural
networks (SNNs) have gained the attention of many researchers. Variational
autoencoders (VAEs), as one of the most popular image generation models, have
attracted a lot of work exploring their SNN implementation. Due to the
constrained binary representation in SNNs, existing SNN VAE methods implicitly
construct the latent space by an elaborated autoregressive network and use the
network outputs as the sampling variables. However, this unspecified implicit
representation of the latent space will increase the difficulty of generating
high-quality images and introduces additional network parameters. In this
paper, we propose an efficient spiking variational autoencoder (ESVAE) that
constructs an interpretable latent space distribution and design a
reparameterizable spiking sampling method. Specifically, we construct the prior
and posterior of the latent space as a Poisson distribution using the firing
rate of the spiking neurons. Subsequently, we propose a reparameterizable
Poisson spiking sampling method, which is free from the additional network.
Comprehensive experiments have been conducted, and the experimental results
show that the proposed ESVAE outperforms previous SNN VAE methods in
reconstructed & generated images quality. In addition, experiments demonstrate
that ESVAE's encoder is able to retain the original image information more
efficiently, and the decoder is more robust. The source code is available at
https://github.com/QgZhan/ESVAE.