用高斯隐空间表示直接优化袋中类别比例，无需中间分类器。
Quantification via Gaussian Latent Space Representations
- 用高斯分布建模隐空间，生成袋的不变表示
- 在多个数据集上超越传统与深度学习方法
- 适合需要直接估计类别比例的场景
量化（或先验概率估计）是预测未知样本袋中各类别比例的任务。现有方法大多依赖先验概率偏移假设，通过底层分类器的预测来构建最优估计模型。本文提出一种端到端神经网络，利用隐空间中的高斯分布表示样本袋，实现对类别的不变表征。该方法将量化问题转化为深度学习中的直接优化问题，可针对任务设计特定损失函数，无需中间分类器。实验表明，该方法在多个数据集上均达到当前最佳性能，显著优于传统量化方法及其它深度学习方法。所有实验代码已公开于 https://github.com/AICGijon/gmnet。
原文摘要 · Abstract (English)
Quantification, or prevalence estimation, is the task of predicting the prevalence of each class within an unknown bag of examples. Most existing quantification methods in the literature rely on prior probability shift assumptions to create a quantification model that uses the predictions of an underlying classifier to make optimal prevalence estimates. In this work, we present an end-to-end neural network that uses Gaussian distributions in latent spaces to obtain invariant representations of bags of examples. This approach addresses the quantification problem using deep learning, enabling the optimization of specific loss functions relevant to the problem and avoiding the need for an intermediate classifier, tackling the quantification problem as a direct optimization problem. Our method achieves state-of-the-art results, both against traditional quantification methods and other deep learning approaches for quantification. The code needed to reproduce all our experiments is publicly available at https://github.com/AICGijon/gmnet.
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