Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery

2026-02-02

AI Summary

This work proposes the Vector-Quantized Latent Concepts (VQLC) framework to address the high computational cost and limited semantic clarity of existing clustering-based post-hoc concept discovery methods on large-scale data. By leveraging a VQ-VAE architecture, VQLC maps continuous latent representations to discrete concept vectors through a learned codebook, enabling efficient and scalable concept discovery without relying on traditional clustering. This approach effectively mitigates issues of frequency bias and shallow clustering that commonly plague conventional methods. As a result, VQLC achieves substantially improved computational efficiency and scalability in large-scale settings while preserving high interpretability and yielding human-understandable concepts.

Technology Category

Machine Learning: ClusteringComputer Vision: Large Vision ModelsData Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphsWeb Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web textSemantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Deep Learning models encode rich semantic information in their hidden representations. However, it remains challenging to understand which parts of this information models actually rely on when making predictions. A promising line of post-hoc concept-based explanation methods relies on clustering token representations. However, commonly used approaches such as hierarchical clustering are computationally infeasible for large-scale datasets, and K-Means often yields shallow or frequency-dominated clusters. We propose the vector quantized latent concept (VQLC) method, a framework built upon the vector quantized-variational autoencoder (VQ-VAE) architecture that learns a discrete codebook mapping continuous representations to concept vectors. We perform thorough evaluations and show that VQLC improves scalability while maintaining comparable quality of human-understandable explanations.

Problem

• concept discovery
• clustering
• scalability
• post-hoc explanation
• deep learning interpretability

Innovation

• Vector Quantization
• Concept Discovery
• VQ-VAE
• Interpretable AI
• Scalable Clustering