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Structured pruning adapters
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DOI：10.1016/j.patcog.2024.110724.png)
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Abstract
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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 introduce the concept of Structured Pruning Adapters (SPAs), a family of compressing, taskswitching network adapters, that accelerate and specialize networks using tiny parameter sets and structured pruning. Specifically, we propose the Structured Pruning Low-rank Adapter (SPLoRA) and the Structured Pruning Residual Adapter (SPPaRA) and evaluate them on a suite of pruning methods, architectures, and image recognition benchmarks. Compared to regular structured pruning with fine-tuning, SPLoRA improves image recognition accuracy by 6.9% on average for ResNet50 while using half the parameters at 90% pruned weights. Alternatively, a SPLoRA augmented model can learn adaptations with 17 x fewer parameters at 70% pruning with 1.6% lower accuracy. For ViT-b/16 models, SPLoRA improves accuracy by an average of 43%-points at 75% pruned weights while learning 6.8 x fewer parameters. Our experimental code and Python library of adapters are available at www.github.com/lukashedegaard/structured-pruning-adapters.
Keywords:
Transfer learning
Adapters
Pruning
Structured pruning
Parameter efficient
Image classification
Convolutional neural network
Vision transformer
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