Domain adaptation is an attractive approach given the availability of a large
amount of labeled data with similar properties but different domains. It is
effective in image classification tasks where obtaining sufficient label data
is challenging. We propose a novel method, named SELDA, for stacking ensemble
learning via extending three domain adaptation methods for effectively solving
real-world problems. The major assumption is that when base domain adaptation
models are combined, we can obtain a more accurate and robust model by
exploiting the ability of each of the base models. We extend Maximum Mean
Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to
compute the adaptation loss in three base models. Also, we utilize a two-fully
connected layer network as a meta-model to stack the output predictions of
these three well-performing domain adaptation models to obtain high accuracy in
ophthalmic image classification tasks. The experimental results using
Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate
the effectiveness of the proposed model.

提出了一个称为 SELDA 的新方法，通过对三种领域适应方法进行扩展并组合，利用每种基本模型的能力来解决现实问题，采用两个全连接层网络作为元模型来堆叠这三种性能良好的领域适应模型的输出预测，从而在眼科图像分类任务中实现高精度。

眼科图像分类中的深度领域自适应叠层集成学习

Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification

We construct a Wasserstein gradient flow of the maximum mean discrepancy
(MMD) and study its convergence properties.
The MMD is an integral probability metric defined for a reproducing kernel
Hilbert space (RKHS), and serves as a metric on probability measures for a
sufficiently rich RKHS. We obtain conditions for convergence of the gradient
flow towards a global optimum, that can be related to particle transport when
optimizing neural networks.
We also propose a way to regularize this MMD flow, based on an injection of
noise in the gradient. This algorithmic fix comes with theoretical and
empirical evidence. The practical implementation of the flow is
straightforward, since both the MMD and its gradient have simple closed-form
expressions, which can be easily estimated with samples.

本文构建了最大均值差异（MMD）的 Wasserstein 梯度流并研究了其收敛性质，提出了一个基于梯度注入噪声的方法进行正则化，并给出了其理论和实证证据。该流的实现很简单，因为 MMD 及其梯度均具有简单的封闭形式表达式，可通过样本轻松估计。