Distillation-Guided Structural Transfer for Continual Learning Beyond Sparse Distributed Memory

• URL: http://arxiv.org/abs/2512.15267v1
• Date: Wed, 17 Dec 2025 10:17:01 GMT
• Title: Distillation-Guided Structural Transfer for Continual Learning Beyond Sparse Distributed Memory
• Abstract summary: We propose a structurally guided continual learning framework that treats distillation not as a regularizer but as a topology-aligned information conduit.<n>SSD identifies neurons with high activation frequency and selectively distills knowledge within previous Top-Kworks and output logits, without requiring replay or task labels.
• Score: 29.87863386264415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse neural systems are gaining traction for efficient continual learning due to their modularity and low interference. Architectures such as Sparse Distributed Memory Multi-Layer Perceptrons (SDMLP) construct task-specific subnetworks via Top-K activation and have shown resilience against catastrophic forgetting. However, their rigid modularity limits cross-task knowledge reuse and leads to performance degradation under high sparsity. We propose Selective Subnetwork Distillation (SSD), a structurally guided continual learning framework that treats distillation not as a regularizer but as a topology-aligned information conduit. SSD identifies neurons with high activation frequency and selectively distills knowledge within previous Top-K subnetworks and output logits, without requiring replay or task labels. This enables structural realignment while preserving sparse modularity. Experiments on Split CIFAR-10, CIFAR-100, and MNIST demonstrate that SSD improves accuracy, retention, and representation coverage, offering a structurally grounded solution for sparse continual learning.

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