Diverse and consistent multi-view networks for semi-supervised regression

Label collection is costly in many applications, which poses the need for label-efficient learning. In this work, we present Diverse and Consistent Multi-view Networks (DiCoM)—a novel semi-supervised regression technique based on a multi-view learning framework. DiCoM combines diversity with consistency—two seemingly opposing yet complementary principles of multi-view learning—based on underlying probabilistic graphical assumptions. Given multiple deep views of the same input, DiCoM encourages a negative correlation among the views’ predictions on labeled data, while simultaneously enforces their agreement on unlabeled data. DiCoM can utilize either multi-network or multi-branch architectures to make a trade-off between computational cost and modeling performance. Under realistic evaluation setups, DiCoM outperforms competing methods on tabular, time series and image data. Our ablation studies confirm the importance of having both consistency and diversity.