InnerCIL: Internal Neuron Representations for Analytic Class-Incremental Learning

Class-incremental learning aims to enable a model to sequentially learn new classes while preserving its ability to recognize old ones. Recently, analytic learning methods have achieved advanced performance by updating classifiers via accumulated features, with the pretrained backbone frozen. However, existing approaches typically focus on transforming or resampling the output features during analysis, where transforming and resampling can introduce feature drift and feature noise respectively, limiting the robustness of the obtained classifier. To address this problem, we propose InnerCIL, where the Internal Neuron Representations are exploited to provide informative and stable information for classifier analysis. InnerCIL consists of two key components, Attention-conditioned Native Dictionary Generation (ANDG) and Fisher Refinement Analytic Classification (FRAC). Specifically, instead of merely utilizing output features, ANDG introduces a Neuron-guided attention mechanism to extract a set of local representations within middle layers. These features form a native dictionary where no additional transform is adopted. Then, based on the dictionary containing local and global features of different stages, FRAC filters a subset to accelerate classifier analysis while mitigating the noise samples that can misguide the classifier. As a result, InnerCIL is both effective and efficient. Experiments on different backbones and benchmarks show that our method outperforms existing methods by a large margin.