Joint domain adaptation aims to learn a high-quality classifier for an unlabeled dataset with the help of auxiliary data. Most methods reduce domain shifts through some carefully designed distance measures. Adversarial learning, which is rarely used for joint domain adaptation, can learn more transferable features while avoiding explicit distance measures. However, it usually suffers from a gradient vanishing problem during the training process. In order to solve the above problems, we propose a novel adversarial joint domain adaptation method, namely Asymmetric Feature mapping based on Least Squares Distance (AFLSD), which consists of asymmetric marginal distribution alignment and conditional distribution alignment. The asymmetric feature mapping, which can get closer features with more flexible parameters, is optimized by the least squares distance to reduce the gradient vanishing problem. The results of classification and other comparative experiments show that AFLSD is superior to the most advanced domain adaptation methods.

联合域自适应旨在借助辅助数据为未标记的数据集学习高质量的分类器。大多数方法通过一些精心设计的距离度量来减少域偏移。对抗学习（很少用于联合域适应）可以学习更多可传递的特征，同时避免显式的距离度量。但是，在训练过程中通常会遇到梯度消失的问题。为了解决上述问题，我们提出了一种新的对抗联合域自适应方法，即基于最小二乘距离的非对称特征映射（AFLSD），它由不对称边际分布对齐和条件分布对齐组成。非对称特征映射，可以通过更灵活的参数获得更接近的特征，通过最小二乘方距离优化来减少梯度消失问题。分类和其他比较实验的结果表明，AFLSD优于最先进的域自适应方法。