Feature Drift Oriented Distribution Reconstruction for Imbalanced Class Incremental Learning

Replay-based class incremental learning (CIL) mitigates catastrophic forgetting by retaining limited historical data. However, the class imbalance between historical and new class data prevents catastrophic forgetting from being fully resolved. Existing approaches rely solely on buffer data to approximate the historical distribution, neglecting the incomplete historical distribution modeling caused by feature drift in the evolving feature space, limiting their effectiveness. To address this challenge, we propose a novel Feature Drift oriented Distribution Reconstruction (FDDR) framework, which reconstructs complete historical distributions utilizing both historical and new knowledge. Specifically, FDDR mitigates the structure degradation of historical distributions caused by feature drift by aligning distributions between the current and previous feature spaces, reserving sufficient feature spaces for subsequent distribution reconstruction. Based on the preserved space, FDDR supplements the sparse historical distribution by incorporating weighted new-class distributions according to inter-class relationships, which are derived from joint prototype cosine similarities. In particular, each joint prototype is constructed from both evolved historical prototypes and buffer prototypes, thereby yielding a more reliable similarity measure. Finally, building upon the reconstructed distributions, FDDR generates pseudo-features to correct biased classifier and further fine-tunes the feature space to achieve superior feature space reconstruction. Comprehensive experiments on widely adopted CIL benchmarks verify the effectiveness of FDDR. Code is available at https://github.com/njustkmg/ECAI25-FDDR.