Asymmetric Transfer Learning with Deep Gaussian Processes

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Melih Kandemir ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:730-738, 2015.

Abstract

We introduce a novel Gaussian process based Bayesian model for asymmetric transfer learning. We adopt a two-layer feed-forward deep Gaussian process as the task learner of source and target domains. The first layer projects the data onto a separate non-linear manifold for each task. We perform knowledge transfer by projecting the target data also onto the source domain and linearly combining its representations on the source and target domain manifolds. Our approach achieves the state-of-the-art in a benchmark real-world image categorization task, and improves on it in cross-tissue tumor detection from histopathology tissue slide images.

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