Improving Interpolation in Autoencoders
Abstract
Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in
a latent code. In some cases, autoencoders can “interpolate”: By decoding the
convex combination of the latent codes for two datapoints, the autoencoder can
produce an output which semantically mixes characteristics from the datapoints. In
this paper, we propose a regularization procedure which encourages interpolated
outputs to appear more realistic by fooling a critic network which has been trained
to recover the mixing coefficient from interpolated data. We then develop a simple
benchmark task where we can quantitatively measure the extent to which various
autoencoders can interpolate and show that our regularizer dramatically improves
interpolation in this setting. We also demonstrate empirically that our regularizer
produces latent codes which are more effective on downstream tasks, suggesting a
possible link between interpolation abilities and learning useful representations.
a latent code. In some cases, autoencoders can “interpolate”: By decoding the
convex combination of the latent codes for two datapoints, the autoencoder can
produce an output which semantically mixes characteristics from the datapoints. In
this paper, we propose a regularization procedure which encourages interpolated
outputs to appear more realistic by fooling a critic network which has been trained
to recover the mixing coefficient from interpolated data. We then develop a simple
benchmark task where we can quantitatively measure the extent to which various
autoencoders can interpolate and show that our regularizer dramatically improves
interpolation in this setting. We also demonstrate empirically that our regularizer
produces latent codes which are more effective on downstream tasks, suggesting a
possible link between interpolation abilities and learning useful representations.
Research Areas
