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Poster
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo · TIAN TIAN · Jiaxin Shi · Jun Zhu · Bo Zhang

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #59
in Thu Poster Session A »

We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i.e., whether a sample pair is in the same cluster. We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset. The two parts share the latent variables. To make the model automatically trade-off between its complexity and fitting data, we also develop its fully Bayesian variant. The challenge of inference is addressed by fast (natural-gradient) stochastic variational inference algorithms, where we effectively combine variational message passing for the relational part and amortized learning of the DGM under a unified framework. Empirical results on synthetic and real-world datasets show that our model outperforms previous crowdsourced clustering methods.

Keywords: Variational Inference · Generative Models · Latent Variable Models
[ Paper]  [ Poster] 

Author Information

Yucen Luo (Tsinghua University)
TIAN TIAN (Tsinghua University)
Jiaxin Shi (Tsinghua University)
Jun Zhu (Tsinghua University)
Bo Zhang (Tsinghua University)

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