In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations. Our method, termed LL-VQ-VAE, replaces the vector quantization layer in VQ-VAE with lattice-based discretization. The learnable lattice imposes a structure over all discrete embeddings, acting as a deterrent against codebook collapse, leading to high codebook utilization. Compared to VQ-VAE, our method obtains lower reconstruction errors under the same training conditions, trains in a fraction of the time, and with a constant number of parameters (equal to the embedding dimension D), making it a very scalable approach. We demonstrate these results on the FFHQ-1024 dataset and include FashionMNIST and Celeb-A.
@article{arxiv.2310.09382,
title = {LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations},
author = {Ahmed Khalil and Robert Piechocki and Raul Santos-Rodriguez},
journal= {arXiv preprint arXiv:2310.09382},
year = {2023}
}