How Does Parameter Pruning Reshape DNN Representations? An Interaction-Driven Exploration
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 not largely affected until the pruning operation begins to remove low-order interactions, and low...
| Authors | Fangbo Li, Junpeng Zhang, Qihan Ren et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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