NI-SSCL: A Neuroplasticity-Inspired Method for Semisupervised Continual Learning

This is the authors' abstract. We don't add key points for this paper.

Deep neural networks face significant challenges in continually acquiring new knowledge due to catastrophic forgetting. Although supervised continual learning has made progress, it is difficult and costly to obtain sufficient labeled data in open environments. To address this, we propose a Neuroplasticity-Inspired method for Semi-Supervised Continual Learning (NI-SSCL), which enables deep neural networks to learn from scarce labels while preserving prior knowledge. Inspired by neurogenesis and synaptic metaplasticity, NI-SSCL comprises two core components to balance memory stability and learning plasticity: a Neuron Expansion (NE) module and a Dynamic Memory Constrain (DMC) module. The neuron expansion module dynamically expands the network to acquire new, knowledge-specific feature representations while maintaining feature discrimination. The dynamic memory constraint module leverages synaptic metaplasticity to regulate shared network components, enabling efficient reuse and reducing forgetting. Experimental results on multiple semi-supervised continual learning benchmarks validate the effectiveness of NI-SSCL, which achieves state-of-the-art performance. Additionally, NI-SSCL demonstrates superior feature retention and adaptability, effectively mitigating catastrophic forgetting while leveraging unlabeled data for improved generalization.