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 are preserved; unlike other manifold learning methods, it simply consists of an offline nearest neighbor computation step and a straightforward learning process that does not require mining negative samples to contrast, eigendecompositions, or cumbersome optimization solvers.
More details and evaluation can be found in our