DADP: Dynamic Activity-Dependent Pruning, A Reverse Hebbian-Inspired Structural Pruning Method
Read the original on arXiv Machine Learning →The paper introduces Dynamic Activity-Dependent Pruning (DADP), a biologically inspired pruning technique that uses accumulated pre‑synaptic activations and post‑synaptic error gradients to assess connection importance. Unlike traditional methods that rely on fixed layer budgets or magnitude thresholds, DADP employs a single global threshold, allowing it to dynamically allocate sparsity across a network’s depth and prune neurons and channels during training. Experiments on MLP, VGG‑16, ResNet‑18, BiLSTM‑CRF, and MiniBERT show that DADP matches or surpasses existing pruning methods, achieving 73.67% accuracy at 99% sparsity on ResNet‑18 while preserving feature diversity as measured by Shannon entropy and effective rank.
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