  We consider learning representations (features) in the setting in which we
have access to multiple unlabeled views of the data for learning while only one
view is available for downstream tasks. Previous work on this problem has
proposed several techniques based on deep neural networks, typically involving
either autoencoder-like networks with a reconstruction objective or paired
feedforward networks with a batch-style correlation-based objective. We analyze
several techniques based on prior work, as well as new variants, and compare
them empirically on image, speech, and text tasks. We find an advantage for
correlation-based representation learning, while the best results on most tasks
are obtained with our new variant, deep canonically correlated autoencoders
(DCCAE). We also explore a stochastic optimization procedure for minibatch
correlation-based objectives and discuss the time/performance trade-offs for
kernel-based and neural network-based implementations.
