LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Keywords: Vector-Quantization, VAE, Lattice, Representation Learning, Discrete Latents
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TL;DR: We introduce learnable lattices for efficient discrete representation learning with vector quantization.
Abstract: In this paper we introduce $\text{\textit{learnable lattice}}$ 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.
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Submission Number: 2570
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