Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned
from a well-labeled source domain to an unlabeled target domain. Recently,
adversarial domain adaptation with two distinct classifiers (bi-classifier) has
been introduced into UDA which is effective to align distributions between
different domains. Previous bi-classifier adversarial learning methods only
focus on the similarity between the outputs of two distinct classifiers.
However, the similarity of the outputs cannot guarantee the accuracy of target
samples, i.e., target samples may match to wrong categories even if the
discrepancy between two classifiers is small. To challenge this issue, in this
paper, we propose a cross-domain gradient discrepancy minimization (CGDM)
method which explicitly minimizes the discrepancy of gradients generated by
source samples and target samples. Specifically, the gradient gives a cue for
the semantic information of target samples so it can be used as a good
supervision to improve the accuracy of target samples. In order to compute the
gradient signal of target samples, we further obtain target pseudo labels
through a clustering-based self-supervised learning. Extensive experiments on
three widely used UDA datasets show that our method surpasses many previous
state-of-the-arts. Codes are available at this https URL

本文提出了一种跨域梯度分歧最小化方法，使用聚类自监督学习获得目标伪标签，显式地最小化源样本和目标样本生成的梯度差异，以提高目标样本的准确性，实验证明该方法优于许多先前的最新技术

跨域梯度差异最小化用于无监督域自适应

Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain  Adaptation

Unsupervised domain adaptation challenges the problem of transferring
knowledge from a well-labelled source domain to an unlabelled target domain.
Recently,adversarial learning with bi-classifier has been proven effective in
pushing cross-domain distributions close. Prior approaches typically leverage
the disagreement between bi-classifier to learn transferable representations,
however, they often neglect the classifier determinacy in the target domain,
which could result in a lack of feature discriminability. In this paper, we
present a simple yet effective method, namely Bi-Classifier Determinacy
Maximization(BCDM), to tackle this problem. Motivated by the observation that
target samples cannot always be separated distinctly by the decision boundary,
here in the proposed BCDM, we design a novel classifier determinacy disparity
(CDD) metric, which formulates classifier discrepancy as the class relevance of
distinct target predictions and implicitly introduces constraint on the target
feature discriminability. To this end, the BCDM can generate discriminative
representations by encouraging target predictive outputs to be consistent and
determined, meanwhile, preserve the diversity of predictions in an adversarial
manner. Furthermore, the properties of CDD as well as the theoretical
guarantees of BCDM's generalization bound are both elaborated. Extensive
experiments show that BCDM compares favorably against the existing
state-of-the-art domain adaptation methods.

本文提出了一个简单而有效的方法 —— 双分类器决定性最大化（BCDM），通过鼓励目标预测输出结果的一致性和确定性，同时以敌对方式维护预测多样性，来解决在目标领域中分类器决定性不足导致的特征区分度不足问题，实现从一个有标签的源域迁移到一个无标签的目标域的无监督领域自适应问题。