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How Does Parameter Pruning Reshape DNN Representations? An Interaction-Driven Exploration

arXiv · AI, language, vision and robotics · article · Sep 6, 2026 · UTC

This study focuses on the scientific problem of understanding internal factors that govern the diverse performance degradation of deep neural networks (DNNs) when different parameters are pruned. In order to explain why pruning certain parameters leads to significant performance degradation but pruning other parameters does not, we examine how the pruning operation affects the interaction patterns encoded by the DNN. We find that when we progressively increase the pruning ratio, the interaction patterns encoded by DNNs exhibit a distinct three-phase dynamics, \emph{i.e.}, model performance is

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