We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lukashedegaard/structured-pruning-adapters | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| bkron | Ran | lukashedegaard/structured-pruning-adapters/sp_adapters/utils.py code served (permissive licence) · get_code("b408fc9c1069786e") |
| recursive_replace | Ran | lukashedegaard/structured-pruning-adapters/sp_adapters/utils.py code served (permissive licence) · get_code("caf5487bfbb58b14") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Adapters are a parameter-efficient alternative to fine-tuning, which augment a frozen base network to learn new tasks. Yet, the inference of the adapted model is often slower than the corresponding fine-tuned model. To improve on this, we propose Structured Pruning Adapters (SPAs), a family of compressing, task-switching network adapters, that accelerate and specialize networks using tiny parameter sets and structured pruning. Specifically, we propose a channel-based SPA and evaluate it with a suite of pruning methods on multiple computer vision benchmarks. Compared to regular structured pruning with fine-tuning, our channel-SPAs improve accuracy by 6.9% on average while using half the parameters at 90% pruned weights. Alternatively, they can learn adaptations with 17x fewer parameters at 70% pruning with 1.6% lower accuracy. Similarly, our block-SPA requires far fewer parameters than pruning with fine-tuning. Our experimental code and Python library of adapters are available at github.com/lukashedegaard/structured-pruning-adapters.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.10155")
get_code_for_paper("2211.10155")
have("2211.10155")
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