  We propose ways to improve the performance of fully connected networks. We
found that two approaches in particular have a strong effect on performance:
linear bottleneck layers and unsupervised pre-training using autoencoders
without hidden unit biases. We show how both approaches can be related to
improving gradient flow and reducing sparsity in the network. We show that a
fully connected network can yield approximately 70% classification accuracy on
the permutation-invariant CIFAR-10 task, which is much higher than the current
state-of-the-art. By adding deformations to the training data, the fully
connected network achieves 78% accuracy, which is just 10% short of a decent
convolutional network.
