标题： 费舍尔深度域适应 显示英文标题

标题： Fisher Deep Domain Adaptation

摘要： 深度领域自适应模型通过利用标记源领域的知识，在未标记目标领域中学习一个神经网络。 这可以通过学习一个领域不变的特征空间来实现。 尽管在源领域中学习到的表示是可分离的，但在目标领域中它们通常具有较大的方差，不同类别标签的样本容易重叠，这会导致次优的适应性能。 为填补这一差距，提出了一种Fisher损失，以学习类内紧凑且类间可分离的判别表示。 在两个基准数据集上的实验结果表明，Fisher损失是深度领域自适应的一种通用且有效的损失。 当与广泛采用的迁移标准结合使用时，会带来显著的改进，包括MMD、CORAL和领域对抗损失。 例如，在Office-Home数据集上，当Fisher损失与领域对抗损失结合使用时，平均准确率绝对提高了6.67%。 显示英文摘要

摘要： Deep domain adaptation models learn a neural network in an unlabeled target domain by leveraging the knowledge from a labeled source domain. This can be achieved by learning a domain-invariant feature space. Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance. To fill the gap, a Fisher loss is proposed to learn discriminative representations which are within-class compact and between-class separable. Experimental results on two benchmark datasets show that the Fisher loss is a general and effective loss for deep domain adaptation. Noticeable improvements are brought when it is used together with widely adopted transfer criteria, including MMD, CORAL and domain adversarial loss. For example, an absolute improvement of 6.67% in terms of the mean accuracy is attained when the Fisher loss is used together with the domain adversarial loss on the Office-Home dataset.