Adaptive Signal Resuscitation: Channel-wise Post-Pruning Repair for Sparse Vision Networks

Magnitude pruning remains a critical bottleneck for deploying sparse vision models at scale, but existing repair methods fail to account for heterogeneous damage across channels within layers. This paper identifies a fundamental granularity mismatch: layer-wise corrections amplify already-collapsed channels while attempting to restore global signal. Adaptive Signal Resuscitation addresses this by applying per-channel variance matching without retraining, enabling high-sparsity networks to retain accuracy where prior methods collapse. For practitioners scaling vision models to edge devices and resource-constrained inference, channel-aware repair could unlock practical deployment of aggressive pruning strategies that current techniques cannot support.

Modelwire context

Explainer

The paper's core insight is that existing post-pruning repair methods operate at the wrong granularity. Layer-wise variance matching treats all channels equally, but pruning damage is heterogeneous across channels within a layer. Channel-wise correction without retraining is the specific technical contribution that prior work missed.

This connects to a broader pattern visible in recent work on learned heterogeneous parameters. The seismic forecasting paper from this week (EarthquakeNet) made the same methodological move: replacing global statistical assumptions with per-location learned parameters because one-size-fits-all models fail on real data. Here, the insight is similar but applied to post-hoc model repair rather than forecasting. Both papers argue that granularity matters, and that domain-specific heterogeneity (spatial in seismology, channel-wise in pruning) is where the actual signal lives. The difference is that Adaptive Signal Resuscitation solves it without retraining, which is a practical constraint the seismic work didn't face.

If practitioners report that channel-wise repair enables 90%+ accuracy retention at 95%+ sparsity on standard vision benchmarks (ImageNet, COCO) without retraining, that validates the claim. If the method only works at moderate sparsity levels (70-80%) or requires task-specific tuning, the practical scope is narrower than the abstract suggests.

Coverage we drew on

Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessment · arXiv cs.LG

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MentionsAdaptive Signal Resuscitation · vision networks · magnitude pruning

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