发表机构
Shenyang Institute of Computing Technology, Chinese Academy of Science; University of Chinese Academy of Science（中国科学院沈阳计算技术研究所; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出AGEDR解耦表示学习框架，通过属性映射嵌入模块实现隐向量解耦，用于医疗声音诊断，性能优于传统分类模型及现有解耦方法，且具备良好的解耦能力与公平性。
AI 中文摘要
深度学习具备强大的特征提取能力，但深度神经网络缺乏公平性与可解释性，限制了其在医疗领域的应用。本文提出一种名为AGEDR（Attributes-based Gaussian Estimation for Disentangled Representation）的解耦表示学习（DisenRL）框架，该框架包含属性映射嵌入（AME）模块，用于将属性映射为向量并与变分自编码器（VAE）中的一部分隐向量对齐；通过最小化互信息，该部分隐向量将与其余隐向量实现解耦。随后利用VAE隐向量的均值参数训练分类器。大量实验表明，AGEDR的性能优于传统分类模型及现有解耦表示学习方法；消融实验也验证了AGEDR的解耦能力与公平性，其源代码可在指定URL获取。
英文摘要
Deep learning has a powerful capability of feature extraction. However, the lack of fairness and interpretability in deep neural networks poses limitations to their adoption in the medical domain. This paper proposes a disentangled representation learning (DisenRL) framework, named the Attributes-based Gaussian Estimation for Disentangled Representation (AGEDR), which incorporates Attribute Mapping Embedding (AME) modules designed to map attributes into vectors and align them with a subset of the latent vectors in a Variational AutoEncoder (VAE). This part of the latent vector will be disentangled from the remaining latent vectors by minimizing mutual information. A classifier is then trained using the mean parameters of the latent vectors from the VAE. Extensive experiments demonstrate that AGEDR outperforms both conventional classification models and existing disentangled representation learning methods. The ablation experiments also indicate the disentangling capability and fairness of AGEDR. The source code is publicly available at https://github.com/ZhaoKe1024/DisentangledRepr.
Comments4 figures, 2 tables, code is available at: https://github.com/ZhaoKe1024/DisentangledRepr
