Reviews: Differentiable Learning of Submodular Functions

This paper proposes a way to differentiate the process of submodular function minimization thus enabling to use these functionals as layers in neural networks. The key insight of the paper consists in the usage of the interpretation of discrete optimization of submodular functions as continuous optimization. As a concrete example the paper studies the CRF for image segmentation and creates and the graphcut layer. This layer is evaluated on the Weizmann dataset for horse segmentation and is reported to bring some improvements. I generally like the paper very much, find the description of the method clear enough.