【摘要】 领域自适应方法在特征变换过程中对多个度量大多采取静态权重设置,导致方法在不同任务上效果差异较大.为此,文中提出领域自适应任务中的动态参数调整方法.基于再生希尔伯特空间模型,最小化域间可区分性联合概率分布差异,求解域间不变特征空间.在此过程中,依据A-距离计算域间差异中同类标签和不同类标签分布差异的占比,并以此动态调整可区分性和可迁移性的权重参数,从而达到最优的自适应效果.在3个图像分类数据集上的实验表明文中方法的有效性.更多还原

【Abstract】 The performance of domain adaptation methods for different tasks is unstable due to its static weight settings for multiple measures during feature shift process. Therefore, a dynamic parameter setting method for domain adaption is proposed. Reproducing Kernel Hilbert space is introduced to learn the invariant space by minimizing the distance between both domains according to the discriminative joint probability distribution. In this process, A-distance is employed to measure the discrepancy ratio of the same labels to the different labels, and this ratio is utilized to adjust the proportion of transferability and discriminability distributions dynamically. With this dynamic parameter settings, better performance is obtained. Experimental results on three image classification datasets show the effectiveness of the proposed method.更多还原