arXiv:2605.13517v2 Announce Type: replace-cross Abstract: 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. Th
The paper builds upon existing VQ-VAE frameworks, indicating an incremental but significant advancement within active research on discrete representation learning for AI models.
Improving VQ-VAE's ability to capture rich and diverse representations can lead to more efficient and capable generative AI models, which impacts multiple AI applications.
The introduction of a spherical angular-margin prior aims to overcome limitations in existing VQ-VAE models, potentially enhancing their capacity for handling complex data.
- · AI researchers
- · Generative AI companies
- · AI model developers
- · Developers relying on less efficient VQ-VAE implementations
Improved generative AI models could produce higher quality and more diverse outputs.
Enhanced model efficiency might reduce computational costs for training and inference in certain applications.
More sophisticated discrete representations could contribute to advances in multimodal AI and complex pattern recognition.
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Read at arXiv cs.LG