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CD-IMM: A Bayesian non-parametric classifier for continual learning
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DOI：10.1016/j.neucom.2026.134751.png)
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Continual learning (CL) refers to the ability of a model to learn from non-stationary data incrementally. Bayesian continual learning methods are increasingly popular, as they naturally support continual learning through online posterior updates. However, many such methods rely on strong assumptions about data drift, limiting their applicability to real-world data streams. In this paper, we first define a framework for CL based on mixture models to highlight the limitations of existing Bayesian CL methods. Then, we propose the Class-Domain Infinite Mixture Model (CD-IMM), a non-parametric Bayesian method that adapts dynamically to increasing data complexity over time. We evaluate CD-IMM on mixed class-domain continual learning scenarios involving both class repetitions and novel domains, and compare it to baselines designed for continual learning with pretrained models. Experimental results show that CD-IMM outperforms strong Bayesian continual learning methods.
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