A Fair Loss Function for Network Pruning

Robbie Meyer, Alexander Wong

Keywords: pruning, fairness, compression, image classification

TL;DR: We can improve the fairness of pruning by modifying the loss function to emphasize samples that were poorly classified by the original model. .

Abstract: Model pruning can enable the deployment of neural networks in environments with resource constraints. While pruning may have a small effect on the overall performance of the model, it can exacerbate existing biases into the model such that subsets of samples see significantly degraded performance. In this paper, we introduce the performance weighted loss function, a simple modified cross-entropy loss function that can be used to limit the introduction of biases during pruning. Experiments using biased classifiers for facial classification and skin-lesion classification tasks demonstrate that the proposed method is a simple and effective tool that can enable existing pruning methods to be used in fairness sensitive contexts.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2211.10285/code)