Class-Incremental Learning via LoRA-based Elastic Ensemble of Experts

Large-scale pre-trained vision models provide powerful representations for class-incremental learning, yet continuously adapting them to new classes without historical samples, task identifiers, or unrestricted parameter growth remains a fundamental challenge. Existing LoRA-based approaches typically follow either a one-adapter-per-task expansion paradigm or a fixed parameter-sharing strategy. The former leads to linear parameter growth as the task sequence expands, while the latter often induces severe cross-task interference. To address these limitations, we propose ${\bf E}^3$ (Elastic Ensemble of Experts), a parameter-efficient framework for class-incremental learning. ${\bf E}^3$ treats LoRA modules as elastic experts whose task capacity is dynamically scheduled according to representational saturation. Within each expert, we further introduce Group-Aware Parameter Partitioning, which allocates LoRA parameters into disjoint task-specific subspaces using magnitude-based importance estimation, thereby mitigating parameter overwriting without additional gradient-sensitivity computation. Moreover, ${\bf E}^3$ incorporates hierarchical moment regularization and orthogonal gradient projection to constrain classifier drift and prevent new-task updates from disrupting historical feature subspaces. Extensive experiments on standard class-incremental learning benchmarks demonstrate that E³ achieves state-of-the-art or competitive performance, especially in long-horizon task sequences, while maintaining a favorable balance between stability, plasticity, and parameter efficiency.