Dynamic cluster memory system

Continual learning has emerged as a critical area in machine learning, enabling models to incrementally assimilate novel information while preserving previously acquired data representations. While most existing research emphasizes supervised learning frameworks, these approaches exhibit limited generalizability to real-world contexts where annotated data is scarce or unavailable. In this research study, we propose the Dynamic Cluster Memory System (DCMS), an innovative architecture designed to facilitate both supervised and unsupervised continual learning by dynamically generating memory clusters that adapt to shifting data distributions. To enhance the scalability of DCMS, we introduce a memory expansion protocol that quantifies representational gaps between incoming data streams and stored exemplars, leveraging this metric to trigger adaptive memory augmentation. Additionally, we propose a sample selection strategy that judiciously allocates new instances to relevant memory clusters, thereby fostering inter-cluster knowledge heterogeneity while also capturing semantically salient features. To address the challenge of memory saturation, we implement a pruning algorithm that systematically eliminates redundant clusters through graph-based relational analysis, ensuring the retention of diverse data within a constrained memory budget. The DCMS framework is inherently model-agnostic and seamlessly integrates with both supervised and unsupervised learning pipelines. Comprehensive experimental evaluations demonstrate that the proposed memory optimization paradigm achieves state-of-the-art results across a range of continual learning benchmarks.