Group-Wise Optimization for Self-Extensible Codebooks in Vector Quantized Models

• URL: http://arxiv.org/abs/2510.13331v2
• Date: Thu, 16 Oct 2025 05:26:09 GMT
• Title: Group-Wise Optimization for Self-Extensible Codebooks in Vector Quantized Models
• Authors: Hong-Kai Zheng, Piji Li,
• Abstract summary: VQ-VAEs leverage self-supervised learning to represent continuous vectors using the closest vectors in a codebook.<n>Existing approaches employ implicit static codebooks or jointly optimize the entire codebook, but these methods constrain the codebook's learning capability.<n>We propose Group-VQ, which performs group-wise optimization on the codebook.
• Score: 22.7968403903992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vector Quantized Variational Autoencoders (VQ-VAEs) leverage self-supervised learning through reconstruction tasks to represent continuous vectors using the closest vectors in a codebook. However, issues such as codebook collapse persist in the VQ model. To address these issues, existing approaches employ implicit static codebooks or jointly optimize the entire codebook, but these methods constrain the codebook's learning capability, leading to reduced reconstruction quality. In this paper, we propose Group-VQ, which performs group-wise optimization on the codebook. Each group is optimized independently, with joint optimization performed within groups. This approach improves the trade-off between codebook utilization and reconstruction performance. Additionally, we introduce a training-free codebook resampling method, allowing post-training adjustment of the codebook size. In image reconstruction experiments under various settings, Group-VQ demonstrates improved performance on reconstruction metrics. And the post-training codebook sampling method achieves the desired flexibility in adjusting the codebook size.

Related papers

• Beyond Stationarity: Rethinking Codebook Collapse in Vector Quantization [12.305907179979426]
  We show that as the encoder drifts, unselected code vectors fail to receive updates and gradually become inactive.<n>To address this, we propose two new methods: Non-Stationary Vector Quantization (NSVQ) and Transformer-based Vector Quantization (TransVQ)<n> Experiments on the CelebA-HQ dataset demonstrate that both methods achieve near-complete codebook utilization and superior reconstruction quality.
  arXiv  Detail & Related papers  (2026-02-21T16:36:50Z)
• Learning Grouped Lattice Vector Quantizers for Low-Bit LLM Compression [57.54335545892155]
  We introduce a Grouped Lattice Vector Quantization (GLVQ) framework that assigns each group of weights a customized lattice codebook.<n>Our approach achieves a better trade-off between model size and accuracy compared to existing post-training quantization baselines.
  arXiv  Detail & Related papers  (2025-10-23T20:19:48Z)
• Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization [60.294965457786844]
  Vector quantization (VQ) is a key component in discrete tokenizers for image generation.<n>VQBridge is a robust, scalable, and efficient projector based on the map function method.<n>FVQ attains 100% codebook usage even with a 262k-codebook.
  arXiv  Detail & Related papers  (2025-09-12T11:08:21Z)
• Dual Codebook VQ: Enhanced Image Reconstruction with Reduced Codebook Size [0.0]
  Vector Quantization (VQ) techniques face challenges in codebook utilization, limiting reconstruction fidelity in image modeling.<n>We introduce a Dual Codebook mechanism that effectively addresses this limitation by partitioning the representation into complementary global and local components.<n>Our approach achieves significant FID improvements across diverse image domains, particularly excelling in scene and face reconstruction tasks.
  arXiv  Detail & Related papers  (2025-03-13T19:31:18Z)
• Scalable Image Tokenization with Index Backpropagation Quantization [74.15447383432262]
  Index Backpropagation Quantization (IBQ) is a new VQ method for the joint optimization of all codebook embeddings and the visual encoder.<n>IBQ enables scalable training of visual tokenizers and, for the first time, achieves a large-scale codebook with high dimension ($256$) and high utilization.
  arXiv  Detail & Related papers  (2024-12-03T18:59:10Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations [0.0]
  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. 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.
  arXiv  Detail & Related papers  (2023-10-13T20:03:18Z)
• EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders [11.086500036180222]
  Codebook collapse is a common problem in training deep generative models with discrete representation spaces. We propose a novel way to incorporate evidential deep learning (EDL) instead of softmax to combat the codebook collapse problem of dVAE.
  arXiv  Detail & Related papers  (2023-10-09T13:39:26Z)
• Online Clustered Codebook [100.1650001618827]
  We present a simple alternative method for online codebook learning, Clustering VQ-VAE (CVQ-VAE) Our approach selects encoded features as anchors to update the dead'' codevectors, while optimising the codebooks which are alive via the original loss. Our CVQ-VAE can be easily integrated into the existing models with just a few lines of code.
  arXiv  Detail & Related papers  (2023-07-27T18:31:04Z)
• Exploring Continual Learning for Code Generation Models [80.78036093054855]
  Continual Learning (CL) is an important aspect that remains underexplored in the code domain. We introduce a benchmark called CodeTask-CL that covers a wide range of tasks, including code generation, translation, summarization, and refinement. We find that effective methods like Prompt Pooling (PP) suffer from catastrophic forgetting due to the unstable training of the prompt selection mechanism.
  arXiv  Detail & Related papers  (2023-07-05T16:58:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.