Cross-domain classificationDeep learningMulti-source domain adaptation (MSDA) dedicates to establishing knowledge transfer from multiple labeled source domains to an unlabeled target domain. Although data from multiple source domains can provide rich information, it also brings two problems. First, it is...
, namely Mutual Learning Net-work for Multi-Source Domain Adaptation (ML-MSDA).As the multiple source domains have different distribu-tions, ML-MSDA trains one separate conditional adversar-ial adaptation network, referred to as branch network, toalign each source domain with the target domain....
Mitigating this matter, Multi-Source Domain Adaptation (MSDA) has been advanced. However, they exhibit performance that falls short of expectations, necessitate complex preparations and lack solid theoretical underpinnings. Concerning these insufficiencies, we propose an innovative MSDA algorithm, ...
: We formulate the facial emotion recognition with scarce data as a multi-source domain adaptation (MSDA) problem, in which there are N labelled source domains and one target domain with few labelled samples. Let the input space be X
To mitigate these problems, we propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework via exploring interactions among domains. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically...
Feature-level and Task-specific Distribution alignment multi-source domain adaptation (FTD-MSDA) framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Sli...
kamiak_train_msda_upper.srun kamiak_train_ssda.srun kamiak_train_ssda_upper.srun kamiak_upload.sh load_datasets.py main.py main_eval.py methods.py metrics.py models.py multiple_inheritance_check.py plots.py pool.py print_dictionary.py
Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers. Multi-source unsupervised domain adaptation (MSDA) aims at learning a predictor for an unlabeled domain by assigning weak knowledge from a bag of source ... LFAE Silva,DCG Pedronette,FA Faria,... 被引量: 0发...
Feature-level and Task-specific Distribution alignment multi-source domain adaptation (FTD-MSDA) framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Sli...
Domain adaptation (DA) offers a valuable means to reuse data and models for new problem domains. However, robust techniques have not yet been considered for time series data with varying amounts of data availability. In our paper, we make three main contributions to fill this gap. First, we...