Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks when massive labeled data are available in the source domain but very few labeled samples are provided in the target domain. Existing solutions usually focus on feature alignment between the two domains while paying little attention to the discrimination capability of learned representations in the target domain. In this paper, we present a novel and effective method, namely Effective Label Propagation (ELP), to tackle this problem by using effective inter-domain and intra-domain semantic information propagation. For inter-domain propagation, we propose a new cycle discrepancy loss to encourage consistency of semantic information between the two domains. For intra-domain propagation, we propose an effective self-training strategy to mitigate the noises in pseudo-labeled target domain data and improve the feature discriminability in the target domain. As a general method, our ELP can be easily applied to various domain adaptation approaches and can facilitate their feature discrimination in the target domain. Experiments on Office-Home and DomainNet benchmarks show ELP consistently improves the classification accuracy of mainstream SSDA methods by 2%~3%. Additionally, ELP also improves the performance of UDA methods as well (81.5% vs 86.1%), based on UDA experiments on the VisDA-2017 benchmark. Our source code and pre-trained models will be released soon.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain Adaptationimage-classificationImage ClassificationSemi-supervised Domain AdaptationSimilar Papers 제목 키워드 기반
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning
Domain adaptation manages to transfer the knowledge of well-labeled source data to unlabeled target data. Many recent efforts focus on improving the prediction accuracy of target pseudo-labels to reduce conditional distr…
Domain AdaptationSelf-LearningSemi-supervised Domain AdaptationTransfer LearningSemi-supervised learning combining backpropagation and STDP: STDP enhances learning by backpropagation with a small amount of labeled data in a spiking neural network
A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learning by spike-timing-dependent plasticity …
Graph-based Label Propagation for Semi-Supervised Speaker Identification
Speaker identification in the household scenario (e.g., for smart speakers) is typically based on only a few enrollment utterances but a much larger set of unlabeled data, suggesting semisupervised learning to improve sp…
Speaker IdentificationSpeaker RecognitionSemisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed
Graph-based semi-supervised learning is a fundamental machine learning problem, and has been well studied. Most studies focus on homogeneous networks (e.g. citation network, friend network). In the present paper, we prop…
Classificationdomain classificationGeneral ClassificationregressionTwo-View Label Propagation to Semi-supervised Reader Emotion Classification
In the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled data …
ClassificationEmotion ClassificationGeneral ClassificationVocal Bursts Valence Prediction