Deep Model Pruning without Finetuning for Few Category Datasets

Abstract

Current deep model pruning methods mainly focus on large datasets and often involve finetuning before deployment. However, in real-world applications, pruning is typically needed for open scenarios with few classification categories, where fine-tuning should be avoided to preserve the generalization of deep models. To address these issues, we propose a novel deep model pruning method called Cluster-based Redundancy Elimination (CRE). Specifically, CRE first represents each convolutional kernel as a point in high-dimensional space. Second, a distance-based strategy is employed to compute a clustering radius for each convolutional layer. Finally, based on these clustering radii, core point filters are selected for pruning, as they extract redundant information that can be captured by neighboring filters in the high-dimensional space. Thus, finetuning deep models to recover their performance can be removed. Comprehensive experiments on five datasets with few categories validate the effectiveness of our approach and demonstrate its superiority over several state-of-the-art pruning methods.