This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is homophilous, where linked nodes often belong to the same class or have similar features. However, such assumptions of ...
Inferring substitutable and complementary items is an important and fundamental concern for recommendation in e-commerce web-sites. However, the item relationships in real-world are usually heterogeneous, posing great challenges to conventional methods that can only deal with homogeneous relationships. More...
Decoupling Anomaly Discrimination and Representation Learning: Self-supervised Learning for Anomaly Detection on Attributed Graph Anomaly detection on attributed graphs is a crucial topic for practical applications. Existing methods suffer from semantic mixture and imbalance issue bec... YM Hu,C Chen,BW ...
Gong, K.; Liang, X.; Zhang, D.; Shen, X.; Lin, L. Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 932–...
In the experiments with each set of images, the training process was carried out ten times, so the results shown in the graphs represent the average performance over those ten runs. In those experiments, for comparison purposes, FDEKF uses the average loss value of every 256 samples, which ...