We introduce an adversarial-critic-and-assistant (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.
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