几篇最新的研究论文探讨了AI模型向量量化技术的进展。ArcVQ-VAE引入了球形角度裕度先验，以提高图像建模中的潜在表示多样性和码本利用率。高斯VAE被用于一种无需训练的方法（Gaussian Quant）中，将其转换为VQ-VAE，性能优于现有方法。DiVeQ提供了一种使用重参数化技巧进行向量量化端到端训练的可微分方法，提高了压缩和生成任务的性能。MGVQ通过集成多维敏感度感知和梯度-Hessian融合来实现超低比特量化，专注于压缩视觉-语言模型。最后，通道式向量量化（CVQ）提出了一种新颖的图像标记范式，对特征图的每个通道进行量化，从而提高了重建和文本到图像生成的效果。另一篇论文详细介绍了使用pgvector驱动的向量搜索系统的编码指南，展示了其在各种AI应用中的实用性。
AI
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 codeboo…
arXiv cs.LG
TIER_1English(EN)·Tongda Xu, Wendi Zheng, Jiajun He, Jose Miguel Hernandez-Lobato, Yan Wang, Ya-Qin Zhang, Jie Tang·
arXiv:2512.06609v3 Announce Type: replace Abstract: Vector-quantized variational autoencoders (VQ-VAEs) are discrete autoencoders that compress images into discrete tokens. However, they are difficult to train due to discretization. In this paper, we propose a simple yet effectiv…
arXiv cs.LG
TIER_1English(EN)·Mohammad Hassan Vali, Tom B\"ackstr\"om, Arno Solin·
arXiv:2509.26469v4 Announce Type: replace Abstract: Vector quantization is common in deep models, yet its hard assignments block gradients and hinder end-to-end training. We propose DiVeQ, which treats quantization as adding an error vector that mimics the quantization distortion…
arXiv:2605.24019v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) achieve outstanding performance, yet their huge model size severely hinders deployment on edge devices with limited resources. As an efficient model compression technique, vector quantization (VQ) exc…
arXiv:2605.26089v1 Announce Type: cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantization, which assigns a discrete token to each patch…
arXiv:2605.24144v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved impressive performance across diverse domains but remain inefficient during the autoregressive decoding phase. Unlike the prefill stage, which employs compute-bound GEMM operations, decod…
We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantization, which assigns a discrete token to each patch feature vector, CVQ quantizes each channel of the…
Channel-wise Vector Quantization replaces patch-wise tokens with channel-wise tokens in image tokenization, enabling a next-channel prediction framework that generates images by sequentially refining visual details.
<p>In this tutorial, we build a complete pgvector playground inside Google Colab and explore how PostgreSQL can work as a powerful vector database for modern AI applications. We start by installing PostgreSQL, compiling the pgvector extension, connecting through Psycopg, and regi…
