Siamese Network Based Multi-Scale Self-Supervised Heterogeneous Graph Representation Learning
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DOI: 10.1109/access.2022.3187088
Abstract: Owing to label-free modeling of complex heterogeneity, self-supervised heterogeneous graph representation learning (SS-HGRL) has been widely studied in recent years. The goal of SS-HGRL is to design an unsupervised learning framework to represent complicated heterogeneous… read more here.
Keywords: heterogeneous graph; representation learning; supervised heterogeneous; self supervised ... See more keywords
Semi-supervised Heterogeneous Domain Adaptation: Theory and Algorithms.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2022.3146234
Abstract: Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from… read more here.
Keywords: heterogeneous domain; domain adaptation; semi supervised; domain ... See more keywords