Paper tables with annotated results for Decoupled Self-supervised Learning for Non-Homophilous Graphs
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 ...
Wavlm: Large-scale self-supervised pre-training for full stack speech processing. IEEE Journal of Selected Topics in Signal Processing, 16(6):1505–1518, 2022. 7 [5] Philippe G Ciarlet and Pierre-Arnaud Raviart. A mixed finite element method for the biharmonic equation. In Mathematical ...
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 ...