Recently, neural networks have gained attention for creating parametric and
invertible multidimensional data projections. Parametric projections allow for
embedding previously unseen data without recomputing the projection as a whole,
while invertible projections enable the generation of new data points. However,
these properties have never been explored simultaneously for arbitrary
projection methods. We evaluate three autoencoder (AE) architectures for
creating parametric and invertible projections. Based on a given projection, we
train AEs to learn a mapping into 2D space and an inverse mapping into the
original space. We perform a quantitative and qualitative comparison on four
datasets of varying dimensionality and pattern complexity using t-SNE. Our
results indicate that AEs with a customized loss function can create smoother
parametric and inverse projections than feed-forward neural networks while
giving users control over the strength of the smoothing effect.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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