This study proposes a method of domain-invariant feature learning for UDA, whose architecture, named MMDDCDA, comprises an MMD-D module and a cross domain adaptation (CDA) module. MMDDCDA performs alternating training similar to adversarial training to alternately boost the power of the two ...
我们将描述这些类别中的每一个以及属于这些类别的最新方法。 3.1 Domain-invariant feature learning 最近的域适应方法通过创建域不变特征表示来对齐源域和目标域,通常采用特征提取器神经网络的形式。如果无论输入数据来自源域还是目标域,特征都遵循相同的分布,则特征表示是域不变的。 如果可以使用域不变特征训练分类器...
一般是用一个 reconstruction network ,读取extractor的feature输出,然后重构extractor的input。如deep reconstruction-classification networks (DRCN)、domain separation networks (DSN)。 Adversarial 通常包括两种方法: learning an approximate Wasserstein distance GAN Domain Mapping 创建一个 domain-invariant feature represe...
因此先学domain-specific表示，再用总的表示减去domain-specific表示，就可以得到domain-invariant表示。
Unsupervised domain adaptation (UDA) mainly explores how to learn domain-invariant features from the source domain when the target domain label is unknown. To learn domain-invariant features requires aligning the distribution of samples from two domains in the feature space, which can be achieved by...
Invariant Risk Minimization(IRM). IRM 要解决如下问题: 即他要学习一个 encoder 参数,这个 encoder 对所有的分类器参数都同时是最优的。为了完成这个目标,encoder 需要抛弃掉 spurious feature。但是这个优化形式 bi-level 的,非常难解决,因此他又提出了一个近似的 target。
1.Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned...
featurelearningmethodarepro-posed,bothofwhichguaranteethedomaininvariantfeatureswithbetterintra-classcompactnessandinter-classseparabil-ity.Extensiveexperimentsshowthatlearningthediscrimi-nativefeaturesinthesharedfeaturespacecansignificantlyboosttheperformanceofdeepdomainadaptationmethods.IntroductionDomainadaptation,which...
我的理解，domain就是能取哪些值，分布就是取不同值的概率。在我们谈及一个函数时，domain就是定义域...
Sparse Invariant Risk Minimization 论文链接: https://proceedings.mlr.press/v162/zhou22e.html IRM 是这两年流行起来的一种 OOD 问题的新范式,IRM 的关键思想是学习从多个环境中提取的数据集上的不变特征表示,基于这种表示,人们应该能够学习在所有这些环境中工作良好的通用分类器。由于模型在这些现有环境中取得了一...