Abstract
We introduce an adversarial-critic-andassistant (ACA) learning framework to
improve the performance of existing supervised
learning with multiple outputs. The core
contribution of our ACA is the innovation
of two novel modules, i.e. an ‘adversarial
critic’ and a ‘collaborative assistant’, that
are jointly designed to provide augmenting
information for facilitating general learning
tasks. Our approach is not intended to be
regarded as an emerging competitor for tons
of well-established algorithms in the field.
In fact, most existing approaches, while implemented with different learning objectives,
can all be adopted as building blocks seamlessly integrated in the ACA framework to
accomplish various real-world tasks. We show
the performance and generalization ability
of ACA on diverse learning tasks including
multi-label classification, attributes prediction
and sequence-to-sequence generation