We propose to address the open set domain adaptation problem by aligning images at both the pixel space and the feature space.Our approach,called Open Set Translation and Adaptation Network(OSTAN),consists of two main...We propose to address the open set domain adaptation problem by aligning images at both the pixel space and the feature space.Our approach,called Open Set Translation and Adaptation Network(OSTAN),consists of two main components:translation and adaptation.The translation is a cycle-consistent generative adversarial network,which translates any source image to the“style”of a target domain to eliminate domain discrepancy in the pixel space.The adaptation is an instance-weighted adversarial network,which projects both(labeled)translated source images and(unlabeled)target images into a domain-invariant feature space to learn a prior probability for each target image.The learned probability is applied as a weight to the unknown classifier to facilitate the identification of the unknown class.The proposed OSTAN model significantly outperforms the state-of-the-art open set domain adaptation methods on multiple public datasets.Our experiments also demonstrate that both the image-to-image translation and the instance-weighting framework can further improve the decision boundaries for both known and unknown classes.展开更多
相同应用领域,不同时间、地点或设备检测到的数据域不一定完整.文中针对如何进行数据域间知识传递问题,提出相同领域的概率分布差异可用两域最小包含球中心点表示且其上限与半径无关的定理.基于上述定理,在原有支持向量域描述算法基础上...相同应用领域,不同时间、地点或设备检测到的数据域不一定完整.文中针对如何进行数据域间知识传递问题,提出相同领域的概率分布差异可用两域最小包含球中心点表示且其上限与半径无关的定理.基于上述定理,在原有支持向量域描述算法基础上,提出一种数据域中心校正的领域自适应算法,并利用人造数据集和KDD CUP 99入侵检测数据集验证该算法.实验表明,这种领域自适应算法具有较好的性能.展开更多
为了给说话人识别系统的应用提供一个较为重要的技术途径,利用美国TI公司生产的TMS320VC5402DSP作为CPU开发的DSP(D igital S ignal Processor)系统,实时实现了一个基于说话人自适应的开集说话人识别系统。为了提高系统的处理速度和识...为了给说话人识别系统的应用提供一个较为重要的技术途径,利用美国TI公司生产的TMS320VC5402DSP作为CPU开发的DSP(D igital S ignal Processor)系统,实时实现了一个基于说话人自适应的开集说话人识别系统。为了提高系统的处理速度和识别的准确性,系统采用少量的语音数据产生说话人模型,在改进的矢量量化方法的基础上,利用一种说话人自适应的阈值处理算法,有效地提高了系统的识别率。同时对降低算法的计算量、数据的存储量进行了较深入的研究。从说话人识别的响应时间、训练时间等综合方面考虑,使真正意义上的说话人识别系统在DSP芯片上实现成为可能。实验表明,该系统在普通机房条件下,可以取得较好的实验效果,系统识别时间小于1 s,完全满足实时性的要求。展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.62032011 and 61772257.
文摘We propose to address the open set domain adaptation problem by aligning images at both the pixel space and the feature space.Our approach,called Open Set Translation and Adaptation Network(OSTAN),consists of two main components:translation and adaptation.The translation is a cycle-consistent generative adversarial network,which translates any source image to the“style”of a target domain to eliminate domain discrepancy in the pixel space.The adaptation is an instance-weighted adversarial network,which projects both(labeled)translated source images and(unlabeled)target images into a domain-invariant feature space to learn a prior probability for each target image.The learned probability is applied as a weight to the unknown classifier to facilitate the identification of the unknown class.The proposed OSTAN model significantly outperforms the state-of-the-art open set domain adaptation methods on multiple public datasets.Our experiments also demonstrate that both the image-to-image translation and the instance-weighting framework can further improve the decision boundaries for both known and unknown classes.
文摘相同应用领域,不同时间、地点或设备检测到的数据域不一定完整.文中针对如何进行数据域间知识传递问题,提出相同领域的概率分布差异可用两域最小包含球中心点表示且其上限与半径无关的定理.基于上述定理,在原有支持向量域描述算法基础上,提出一种数据域中心校正的领域自适应算法,并利用人造数据集和KDD CUP 99入侵检测数据集验证该算法.实验表明,这种领域自适应算法具有较好的性能.
