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Incremental face clustering with optimal summary learning via graph convolutional network
2021
Tsinghua Science and Technology
In this study, we address the problems encountered by incremental face clustering. Without the benefit of having observed the entire data distribution, incremental face clustering is more challenging than static dataset clustering. Conventional methods rely on the statistical information of previous clusters to improve the efficiency of incremental clustering; thus, error accumulation may occur. Therefore, this study proposes to predict the summaries of previous data directly from data
doi:10.26599/tst.2020.9010024
fatcat:24bopbcw4rfn7nm5hvldwm5bti