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.

本文研究了多视角数据下的特征表示学习问题，比较了基于神经网络的多种技术，提出了一种新的深度自编码器，通过随机优化方法探索了小批量相关性目标函数，最终发现基于相关性的表现效果最佳。