Bias Alleviation through Network Pruning for Sparse and Debiased Models.

Abstract

Pruning is a highly effective method for reducing the size of neural networks with negligible impact on their average performance. However, recent studies have revealed that pruning actually amplifies the bias in the models, leading to decreased performance for underrepresented groups. To address this issue, we first analyze the impact of pruning on the confidence of each sample and introduce Accumulated Confidence (AC). AC is a proxy that facilitates the identification of bias-conflicting and bias-aligned samples without relying on group annotations. We then propose a debiasing algorithm, which is called DEbiasing Network through Pruning (DENP). DENP utilizes AC to mitigate bias within the network. Even without bias information, DENP exhibits remarkable debiasing performance on varying levels of sparsity, effectively mitigating the bias-exacerbating property of pruning and resulting in both sparse and debiased neural networks. Moreover, even when compared with state-of-the-art debiasing baselines under identical conditions, the DENP still achieves the best performance on multiple benchmark datasets, demonstrating its superior debiasing capabilities.