Gaussian Quantized Variational Bayesian for Continuous and Discrete Generation

We propose a novel vector quantization method, GQ-VAE, which incorporates a dual latent space, regularized to a Gaussian distribution prior to quantization. This design proved highly effective, simultaneously mitigating both posterior and codebook collapse while offering the versatility to support downstream models operating on either continuous or discrete representations.