从源域中选择具有较小的价值差异的transitions来进行dynamic adaptation。 为了从源域中选择合理的transitions,基于transition中的状态-动作对,估计源域和目标域中下一个状态价值之间的差异是否足够小。 为了计算∆(ssrc, asrc),采用了拟合的Q评估来估计价值函数,并使用了目标域数据训练的高斯动态模型ensemble来生成源域...
文章将源域中的状态空间视作对齐表征空间,首先在源域上训练一个policy,通过学习一个从image- space映射到state-space的mapping function,agent就可以直接在目标域上应用这个policy;在本文中模拟器是源域,真实环境为目标域。 方法介绍:Cross-modal Domain Adaptation with Sequential Structure Domain Adaptation in RL as...
Translation and cross-cultural adaptation Translation and back-translation processes were carried out during the preparation of their reports. The changes were minor and agreed by consensus and concerned the response options in the function domain:—In the 1st domain Function: Within the options of th...
Sustainable adaptationSocial networksClimate policyTanzaniaLeast developed countries have prepared national adaptation programs of action (NAPAs) to coordinate international adaptation funding to reduce social vulnerability to climate change. The adaptation programs have been written for consistency with existing ...
Cross-domain sentiment classification aims to predict the sentiment tendency in unlabeled target domain data using labeled source-domain data. The wide range of data sources has motivated research into multi-source cross-domain sentiment classification tasks. Conventional domain adaptation methods focus on...
Model augmentations: Innovations include the Action-based Access Control (RBAC adaptation) for cross-domain needs [43], a Dynamic User Trust-based model (TC-ABAC) for cloud security [44], the Role-based Cross-Domain System Access Control(RBAC-IC) for multi-domain platforms [45], and a uni...
Abdelwahab O, Elmaghraby AS (2018) Deep learning based vs markov chain based text generation for cross domain adaptation for sentiment classification. In: Proceedings of the IEEE international conference on information reuse and integration (IRI), pp 252–255. https://doi.org/10.1109/iri.2018.0004...
Domain adaptation is crucial for transferring the knowledge from the source labeled CT dataset to the target unlabeled MR dataset in abdominal multi-organ segmentation. Meanwhile, it is highly desirable to avoid the high annotation cost related to the target dataset and protect the source dataset pri...
表中的值是agent在目标任务上获得的reward与optimal policy获得的reward的比值,越接近1说明agent表现越接近optimal。结果表明Ours显著优于其他的方法,且接近optimal表现。 下图是以示范数目作为变量的实验,图中self-demo是直接在对agent domain上目标任务的optimal policy进行模仿达到的效果。 下图是以proxy task数目作为...
Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and aim to address the domain gap between the suppo