  Due to the subjective crowdsourcing annotations and the inherent inter-class
similarity of facial expressions, the real-world Facial Expression Recognition
(FER) datasets usually exhibit ambiguous annotation. To simplify the learning
paradigm, most previous methods convert ambiguous annotation results into
precise one-hot annotations and train FER models in an end-to-end supervised
manner. In this paper, we rethink the existing training paradigm and propose
that it is better to use weakly supervised strategies to train FER models with
original ambiguous annotation.
