SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models

Understand

Layer pruning has emerged as a potent approach to remove redundant layers in the pre-trained network on the purpose of reducing network size and improve computational efficiency.

• However, existing layer pruning methods mostly overlook the intrinsic connections and inter-dependencies between different layers within complicated deep neural networks.
• This oversight can result in pruned models that do not preserve the essential characteristics of the pre-trained network as effectively as desired.
• To address these limitations, we propose a Similarity-Guided Layer Partition (SGLP) Pruning, a novel pruning framework that exploits representation similarity to guide efficient and informed layer removal for compressing large deep models.