为了解决这个问题,可以采用域适应技术(domain adaptation)来实现跨域情感分析。具体而言,域适应技术可分为两类工作:基于特征的转移和基于数据的转移。其中,基于特征的转移是指学习领域无关的特征表示。而基于数据的转移则是指利用源域和目标域的未标注数据,通过在两个域之间进行数据转移,使得目标域中的数据能够更好地...
The behavior of users in certain services indicates their preferences, which may be used to make recommendations for other services they have never used. However, the cross-domain relation between items and user preferences is not simple, especially when
Cross-Domain Argument Span Extraction: PDTB - BioDRB we additionally train PDTB models on the automatic features. (通过自动句子分割、标记化和句法分析从PDTB中提取的特征。) 论元跨度提取 比 搜索连接检测和关系sense分类的论述子任务更好地推广到生物医学领域。 Feature-Level Domain Adaptation(特征级领域适应...
Multi-level relation learning for cross-domain few-shot hyperspectral image classification Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and... C Liu,L Yang,Z Li,... - Applied Intelligence: The...
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation. Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation. Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval. OTCE: A Transferability Metric for Cross-Domain Cross-Task Represe...
Entropy based classifier for cross-domain opinion mining In recent years, the growth of social network has increased the interest of people in analyzing reviews and opinions for products before they buy them. Consequently, this has given rise to the domain adaptation as a prominent area of res....
2.2. Domain adaptation Machine learning works based on the assumption that the training and test data are drawn from the identical dis- tribution. It will cause a dramatic performance drop if the training and test datasets have clear domain discrepancy. Doma...
为了缓解这一限制,跨域小样本学习(Cross-domain Few-shot learning,CDFSL)引起了关注,因为它允许源数据和目标数据来自不同的领域和标签空间。本文首次对 CDFSL 进行了全面综述,由于其独特的设定和难点,CDFSL 受到了比 FSL 更少的关注。我们希望这篇论文能够为 CDFSL 研究者提供立场观点和教程。本综述首先介绍了 ...
二、现有问题 三、解决方法&贡献 三、方法架构 3.1图引导的原型对齐 3.2Class-imbalance-aware Adaptation Training 3.3. Two-stage Domain Alignment 四、 总结 【我在近期会做一个系列的2020年左右目标检测域适应的论文阅读分享,这是第二篇要分享的论文】一...
Class-imbalance-aware Adaptation Training 类似于focal loss 通过难以样本与正负样本的权重来缓解类别不平衡的问题,具体操作见原文。 结果 本人跑了其github代码, source domain 数据集为 Sim10K , target domain 数据集为 City 下,map 峰值为 46.8左右,峰值epoch第2个。 相关问题,需要控制source domain 与 targ...