General Partial Label Learning via Dual Bipartite Graph Autoencoder


  • Brian Chen Columbia University
  • Bo Wu Columbia University
  • Alireza Zareian Columbia University
  • Hanwang Zhang Nanyang Technological University
  • Shih-Fu Chang Columbia University



We formulate a practical yet challenging problem: General Partial Label Learning (GPLL). Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervision assumption from instance-level — a label set partially labels an instance — to group-level: 1) a label set partially labels a group of instances, where the within-group instance-label link annotations are missing, and 2) cross-group links are allowed — instances in a group may be partially linked to the label set from another group. Such ambiguous group-level supervision is more practical in real-world scenarios as additional annotation on the instance-level is no longer required, e.g., face-naming in videos where the group consists of faces in a frame, labeled by a name set in the corresponding caption. In this paper, we propose a novel graph convolutional network (GCN) called Dual Bipartite Graph Autoencoder (DB-GAE) to tackle the label ambiguity challenge of GPLL. First, we exploit the cross-group correlations to represent the instance groups as dual bipartite graphs: within-group and cross-group, which reciprocally complements each other to resolve the linking ambiguities. Second, we design a GCN autoencoder to encode and decode them, where the decodings are considered as the refined results. It is worth noting that DB-GAE is self-supervised and transductive, as it only uses the group-level supervision without a separate offline training stage. Extensive experiments on two real-world datasets demonstrate that DB-GAE significantly outperforms the best baseline over absolute 0.159 F1-score and 24.8% accuracy. We further offer analysis on various levels of label ambiguities.




How to Cite

Chen, B., Wu, B., Zareian, A., Zhang, H., & Chang, S.-F. (2020). General Partial Label Learning via Dual Bipartite Graph Autoencoder. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 10502-10509.



AAAI Technical Track: Vision