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Decay Pruning Method: Smooth Pruning With a Self-Rectifying Procedure
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Current structured pruning methods often result in considerable accuracy drops due to abrupt network changes and loss of information from pruned structures. To address these issues, we introduce the Decay Pruning Method (DPM), a novel smooth pruning approach with a self-rectifying mechanism. DPM consists of two key components: (i) Smooth Pruning: It converts conventional single-step pruning into multi-step smooth pruning, gradually reducing redundant structures to zero over N steps with ongoing optimization. (ii) Self-Rectifying: This procedure further enhances the aforementioned process by rectifying sub-optimal pruning based on gradient information. Our approach demonstrates strong generalizability and can be easily integrated with various existing pruning methods. We validate the effectiveness of DPM by integrating it with three popular pruning methods: OTOv2, Depgraph, and Gate Decorator. Experimental results show consistent improvements in performance compared to the original pruning methods, along with further reductions of FLOPs in most scenarios.
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Cited by 1 Pith paper
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Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis
Component-aware dependency graphs that isolate modules and inter-module flows produce finer pruning groups and gentler performance loss on a TD-MPC control task than vanilla dependency-graph pruning.
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