Nguyen, C., Raja, A., Zhang, L., Xu, X., Unnikrishnan, B., Ragab, M., Lu, K., & Foo, C.-S. (2023). Diverse and consistent multi-view networks for semi-supervised regression. Machine Learning. https://doi.org/10.1007/s10994-023-06305-0
Abstract:
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.
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Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funds
Grant Reference no. : A20H6b0151
Description:
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10994-023-06305-0