AI RESEARCH
Deep Kuratowski Embedding Neural Networks for Wasserstein Metric Learning
arXiv CS.LG
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ArXi:2604.04343v1 Announce Type: new Computing pairwise Wasserstein distances is a fundamental bottleneck in data analysis pipelines. Motivated by the classical Kuratowski embedding theorem, we propose two neural architectures for learning to approximate the Wasserstein-2 distance ($W_2$) from data. The first, DeepKENN, aggregates distances across all intermediate feature maps of a CNN using learnable positive weights.