Coupled local–global adaptation for multi-source transfer learning
نویسندگان
چکیده
منابع مشابه
A Representation Learning Framework for Multi-Source Transfer Parsing
Cross-lingual model transfer has been a promising approach for inducing dependency parsers for lowresource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target languagespecific syntactic structures are difficult to be recovered. To address these two c...
متن کاملMulti-Similarity Based Multi-Source Transfer Learning and Its Applications
—In this paper, a novel multi-source transfer learning method based on multi-similarity ((MS)TL) is proposed. First, we measure the similarities between domains at two levels, i.e., “domain-domain” and “sample-domain”. With the multisimilarities, (MS)TL can explore more accurate relationship between the source domains and the target domain. Then, the knowledge of the source domains is transfer...
متن کاملMulti-Source image enhancement via Coupled Dictionary Learning
Motivation. Multi and Hyperspectral remote sensing imagery provide valuable insights regarding the composition of a scene and significantly facilitate tasks like object and material recognition, spectral unmixing and region clustering, among others [1], [2]. However, current remote sensing imaging architectures are unable to concurrently acquire high spatial and spectral resolution imagery, due...
متن کاملAn Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources
This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO...
متن کاملSource Free Transfer Learning for Text Classification
Transfer learning uses relevant auxiliary data to help the learning task in a target domain where labeled data is usually insufficient to train an accurate model. Given appropriate auxiliary data, researchers have proposed many transfer learning models. How to find such auxiliary data, however, is of little research so far. In this paper, we focus on the problem of auxiliary data retrieval, and...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2018
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2017.06.051