Title: Slimming the Giant: Efficient Structured Pruning for Adapter-Tuned SAM
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Structured Pruning, Model Compression, Vision Foundation Models

Abstract: Foundation models with parameter-efficient adapters enable strong segmentation but remain hard to deploy due to scale and cost. We propose Adapter-aware Structured Sparsification (ASSP), a structured pruning framework for adapter-tuned SAM. ASSP begins with a concise dependency analysis of backbone–adapter couplings and derives unified slicing rules for heads, channels, and kernels. It then scores structures via a Projected-Gradient Residual criterion that aligns upstream and downstream gradient subspaces, and restores accuracy with a dual-stream compensation scheme that alternates supervision on both data sources. The procedure runs in two stages: prune and recover adapters, then freeze adapters and prune and recover the backbone. Built on SAM-Med2D, ASSP uses only 20k images (0.4% of SA-Med2D-20M) yet reduces encoder parameters by over 75% and compute to about one quarter, while Dice typically stays within two points of the baseline. Under the same calibration budget it outperforms a transferred SlimSAM baseline and yields consistent latency and throughput gains on H20 GPUs. Although evaluated on medical data, the dependency modeling, PGR scoring, and dual-stream compensation are task-agnostic and broadly applicable to adapter-tuned models.