Jan. 7, 2022, 2:10 a.m. | He Liu, Tao Wang, Congyan Lang, Songhe Feng, Yi Jin, Yidong Li

cs.LG updates on arXiv.org arxiv.org

Differentiable solvers for the linear assignment problem (LAP) have attracted
much research attention in recent years, which are usually embedded into
learning frameworks as components. However, previous algorithms, with or
without learning strategies, usually suffer from the degradation of the
optimality with the increment of the problem size. In this paper, we propose a
learnable linear assignment solver based on deep graph networks. Specifically,
we first transform the cost matrix to a bipartite graph and convert the
assignment task to …

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