  Using noisy crowdsourced labels from multiple annotators, a deep
learning-based end-to-end (E2E) system aims to learn the label correction
mechanism and the neural classifier simultaneously. To this end, many E2E
systems concatenate the neural classifier with multiple annotator-specific
``label confusion'' layers and co-train the two parts in a parameter-coupled
manner. The formulated coupled cross-entropy minimization (CCEM)-type criteria
are intuitive and work well in practice. Nonetheless, theoretical understanding
of the CCEM criterion has been limited. The contribution of this work is
twofold: First, performance guarantees of the CCEM criterion are presented. Our
analysis reveals for the first time that the CCEM can indeed correctly identify
the annotators' confusion characteristics and the desired ``ground-truth''
neural classifier under realistic conditions, e.g., when only incomplete
annotator labeling and finite samples are available. Second, based on the
insights learned from our analysis, two regularized variants of the CCEM are
proposed. The regularization terms provably enhance the identifiability of the
target model parameters in various more challenging cases. A series of
synthetic and real data experiments are presented to showcase the effectiveness
of our approach.
