ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin
Vector Quantized Variational Autoencoder (VQ-VAE) has become a fundamental framework for learning discrete representations in image modeling. However, VQ-VAE models must tokenize entire images using a finite set of codebook vectors, and this capacity limitation restricts their ability to capture rich and diverse representations. In this paper, we propose ArcCosine Additive Margin VQ-VAE (ArcVQ-VAE), a novel vector quantization framework that introduces a spherical angular-margin prior (SAMP) for the codebook of a conventional VQ-VAE. The proposed SAMP consists of Ball-Bounded Norm Regularization, which constrains all codebook vectors within a time-dependent Euclidean ball, and ArcCosine A...
| Authors | Jaeyung Kim, YoungJoon Yoo |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 27 May 2026 |
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