  Recent advances in training deep (multi-layer) architectures have inspired a
renaissance in neural network use. For example, deep convolutional networks are
becoming the default option for difficult tasks on large datasets, such as
image and speech recognition. However, here we show that error rates below 1%
on the MNIST handwritten digit benchmark can be replicated with shallow
non-convolutional neural networks. This is achieved by training such networks
using the 'Extreme Learning Machine' (ELM) approach, which also enables a very
rapid training time (~10 minutes). Adding distortions, as is common practise
for MNIST, reduces error rates even further. Our methods are also shown to be
capable of achieving less than 5.5% error rates on the NORB image database. To
achieve these results, we introduce several enhancements to the standard ELM
algorithm, which individually and in combination can significantly improve
performance. The main innovation is to ensure each hidden-unit operates only on
a randomly sized and positioned patch of each image. This form of random
`receptive field' sampling of the input ensures the input weight matrix is
sparse, with about 90% of weights equal to zero. Furthermore, combining our
methods with a small number of iterations of a single-batch backpropagation
method can significantly reduce the number of hidden-units required to achieve
a particular performance. Our close to state-of-the-art results for MNIST and
NORB suggest that the ease of use and accuracy of the ELM algorithm for
designing a single-hidden-layer neural network classifier should cause it to be
given greater consideration either as a standalone method for simpler problems,
or as the final classification stage in deep neural networks applied to more
difficult problems.
