  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.
