[1]ZHU Guangze,LIU Xingbo,NIE Xiushan,et al.Unsupervised continual learning based hyperbolic product quantization[J].CAAI transactions on intelligent systems,2026,21(5):1237-1248.[doi:10.11992/tis.202507031]
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Unsupervised continual learning based hyperbolic product quantization

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1237-1248 Column: 学术论文—机器感知与模式识别 Public date: 2026-09-05
Title:
Unsupervised continual learning based hyperbolic product quantization
Author(s):
ZHU Guangze;  LIU Xingbo;  NIE Xiushan;  WANG Shaohua
School of Computer and Artificial Intelligence, Shandong Jianzhu University, Jinan 250101, China
Keywords:
continual learning; unsupervised learning; product quantization; codebook; hyperbolic space; Lorentzian distances; contrastive learning; catastrophic forgetting
CLC:
TP391.4
DOI:
10.11992/tis.202507031
Abstract:
Unsupervised continual learning (UCL) is a paradigm for continuously acquiring knowledge from dynamically changing task streams without manual annotations, critical for building intelligent systems with long-term continuous learning capabilities. Existing UCL methods typically suffer from poor early task performance and catastrophic forgetting, stemming from insufficient feature diversity and weak structural alignment. To capture data’s hierarchical semantics and enhance feature diversity, we propose a novel method—hyperbolic product quantization-based unsupervised continual learning (HPQUCL)—the first to introduce hyperbolic space product quantization into UCL. It leverages hyperbolic geometry’s hierarchical properties to boost feature diversity, alleviating poor early task performance. Additionally, a hybrid replay strategy based on hyperbolic distance is designed, selecting structurally representative samples via Lorentz distance for replay to mitigate catastrophic forgetting. A contrastive loss between original and quantized representations in hyperbolic space is also proposed, which further enhances discriminative ability. Experiments on multiple benchmarks show HPQUCL significantly outperforms mainstream UCL methods in knowledge retention and early task performance, demonstrating hyperbolic quantization and structure-aware replay as effective for improving UCL.
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