# Filter Pruning by Switching to Neighboring CNNs with Good Attributes

## TL;DR

This paper introduces Meta-attribute-based Filter Pruning (MFP), a novel method that adaptively selects pruning criteria considering filter relationships and network state, leading to significant FLOPs reduction with minimal accuracy loss.

## Contribution

The paper proposes a new filter pruning approach that incorporates geometric distance and meta-attributes for adaptive criterion selection, improving pruning effectiveness.

## Key findings

- Reduced over 50% FLOPs on ResNet-50 with minimal accuracy loss
- Enhanced filter importance evaluation by considering filter relationships
- Validated on image classification benchmarks

## Abstract

Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same. In addition, when evaluating the filter importance, only the magnitude information of the filters is considered. However, in neural networks, filters do not work individually, but they would affect other filters. As a result, the magnitude information of each filter, which merely reflects the information of an individual filter itself, is not enough to judge the filter importance. To solve the above problems, we propose Meta-attribute-based Filter Pruning (MFP). First, to expand the existing magnitude information based pruning criteria, we introduce a new set of criteria to consider the geometric distance of filters. Additionally, to explicitly assess the current state of the network, we adaptively select the most suitable criteria for pruning via a meta-attribute, a property of the neural network at the current state. Experiments on two image classification benchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reduce more than 50% FLOPs with only 0.44% top-5 accuracy loss.