IDAP++: Advancing Divergence-Based Pruning via Filter-Level and Layer-Level Optimization
To address the coexistence of filter-level and architectural redundancy in neural network compression, this paper proposes a unified pruning framework based on information flow divergence. Methodologically, it unifies fine-grained filter-level pruning and coarse-grained layer-level removal within a single theoretical framework: tensor flow divergence quantifies the contribution of individual filters and entire layers to information propagation; a two-stage iterative optimization is then employed—first performing divergence-aware filter pruning, followed by elimination of inefficient modules based on layer-wise contribution analysis. The framework is architecture-agnostic, applicable to CNNs, Transformers, and other modern architectures. Experiments on mainstream benchmarks demonstrate parameter compression ratios comparable to or exceeding state-of-the-art methods, with negligible accuracy degradation and significantly improved deployment efficiency under resource constraints. The core contribution lies in establishing a cross-granularity, theoretically consistent, and interpretable joint compression paradigm.