  Partial-Label Learning (PLL) is a typical problem of weakly supervised
learning, where each training instance is annotated with a set of candidate
labels. Self-training PLL models achieve state-of-the-art performance but
suffer from error accumulation problem caused by mistakenly disambiguated
instances. Although co-training can alleviate this issue by training two
networks simultaneously and allowing them to interact with each other, most
existing co-training methods train two structurally identical networks with the
same task, i.e., are symmetric, rendering it insufficient for them to correct
each other due to their similar limitations. Therefore, in this paper, we
propose an asymmetric dual-task co-training PLL model called AsyCo, which
forces its two networks, i.e., a disambiguation network and an auxiliary
network, to learn from different views explicitly by optimizing distinct tasks.
Specifically, the disambiguation network is trained with self-training PLL task
to learn label confidence, while the auxiliary network is trained in a
supervised learning paradigm to learn from the noisy pairwise similarity labels
that are constructed according to the learned label confidence. Finally, the
error accumulation problem is mitigated via information distillation and
confidence refinement. Extensive experiments on both uniform and
instance-dependent partially labeled datasets demonstrate the effectiveness of
AsyCo. The code is available at https://github.com/libeibeics/AsyCo.
