Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers

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How researchers see this paper

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GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (2018, methodology): “For example, GraphVAE (Simonovsky & Komodakis, 2018) uses approximate graph matching to address this issue, requiring O(n(4)) operations in the worst case (Cho et al., 2014).”
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (2018, methodology): “It can be expressed as integer quadratic programming problem of similarity maximization over X and is typically approximated by relaxation of X into continuous domain: X ∗ ∈ [0 , 1] k × n (Cho et al., 2014).”
GraphRNN: A Deep Generative Model for Graphs (2018, methodology): “For example, GraphVAE (Simonovsky & Komodakis, 2018) uses approximate graph matching to address this issue, requiring O(n(4)) operations in the worst case (Cho et al., 2014).”
Mining And-Or Graphs for Graph Matching and Object Discovery (2015, background): “Learning graph matching: Graph/image matching theories have been developed for decades [24, 5, 6, 35, 12, 28, 4].”
Learning to Find Good Correspondences (2017, unknown): “More complex strategies [19, 5] could be used, but we found this one sufficient.”