  Convolutional neural networks perform well on object recognition because of a
number of recent advances: rectified linear units (ReLUs), data augmentation,
dropout, and large labelled datasets. Unsupervised data has been proposed as
another way to improve performance. Unfortunately, unsupervised pre-training is
not used by state-of-the-art methods leading to the following question: Is
unsupervised pre-training still useful given recent advances? If so, when? We
answer this in three parts: we 1) develop an unsupervised method that
incorporates ReLUs and recent unsupervised regularization techniques, 2)
analyze the benefits of unsupervised pre-training compared to data augmentation
and dropout on CIFAR-10 while varying the ratio of unsupervised to supervised
samples, 3) verify our findings on STL-10. We discover unsupervised
pre-training, as expected, helps when the ratio of unsupervised to supervised
samples is high, and surprisingly, hurts when the ratio is low. We also use
unsupervised pre-training with additional color augmentation to achieve near
state-of-the-art performance on STL-10.
