TLDR: Twin Learning for Dimensionality Reduction
TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses. Inspired by manifold learning, TLDR uses nearest neighbors as a way to build pairs from a training set and a redundancy reduction loss to learn an encoder that produces representations invariant across such pairs. Similar to other neighborhood embeddings, TLDR effectively and unsupervisedly learns low-dimensional spaces where local neighborhoods of the input space […]
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