RL-TST improves two-sample testing by autoencoding all unlabeled data first, then training a discriminative model on the labeled split, which boosts test power on MNIST, ImageNet, and synthetic benchmarks.
Semi-Supervised Learning
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A Unified Data Representation Learning for Non-parametric Two-sample Testing
RL-TST improves two-sample testing by autoencoding all unlabeled data first, then training a discriminative model on the labeled split, which boosts test power on MNIST, ImageNet, and synthetic benchmarks.