Existing pruning techniques preserve deep neural networks' overall ability to
make correct predictions but may also amplify hidden biases during the
compression process. We propose a novel pruning method, Fairness-aware GRAdient
Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of
pruning on different sub-groups. Our method calculates the per-group importance
of each model weight and selects a subset of weights that maintain the relative
between-group total importance in pruning. The proposed method then prunes
network edges with small importance values and repeats the procedure by
updating importance values. We demonstrate the effectiveness of our method on
four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks
of face attribute classification where our method reduces the disparity in
performance degradation by up to 90% compared to the state-of-the-art pruning
algorithms. Our method is substantially more effective in a setting with a high
pruning rate (99%). The code and dataset used in the experiments are available
at this https URL

本文提出了一种新的剪枝方法，称为公平感知梯度剪枝方法（FairGRAPE），以平衡不同子组在神经网络压缩过程中可能存在的影响，该方法在面部属性分类任务中对四个数据集（FairFace，UTKFace，CelebA 和 ImageNet）进行了测试，在高剪枝率（99%）的情况下比其他最先进的剪枝算法减少了达到 90% 的性能下降差异。

面部属性分类的公平感知梯度修剪方法 FairGRAPE

FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification

Humans focus attention on different face regions when recognizing face
attributes. Most existing face attribute classification methods use the whole
image as input. Moreover, some of these methods rely on fiducial landmarks to
provide defined face parts. In this paper, we propose a cascade network that
simultaneously learns to localize face regions specific to attributes and
performs attribute classification without alignment. First, a weakly-supervised
face region localization network is designed to automatically detect regions
(or parts) specific to attributes. Then multiple part-based networks and a
whole-image-based network are separately constructed and combined together by
the region switch layer and attribute relation layer for final attribute
classification. A multi-net learning method and hint-based model compression is
further proposed to get an effective localization model and a compact
classification model, respectively. Our approach achieves significantly better
performance than state-of-the-art methods on unaligned CelebA dataset, reducing
the classification error by 30.9%.

本文提出了一个级联网络，同时学习了特定属性的面部区域定位和无需对齐的属性分类，使用多网路学习方法和基于提示的模型压缩进一步提高了模型性能。在未对齐的 CelebA 数据集上，与最先进的方法相比，分类错误率降低了 30.9％。