Learning invariant features through local space contraction
  We present in this paper a novel approach for training deterministic
auto-encoders. We show that by adding a well chosen penalty term to the
classical reconstruction cost function, we can achieve results that equal or
surpass those attained by other regularized auto-encoders as well as denoising
auto-encoders on a range of datasets. This penalty term corresponds to the
Frobenius norm of the Jacobian matrix of the encoder activations with respect
to the input. We show that this penalty term results in a localized space
contraction which in turn yields robust features on the activation layer.
Furthermore, we show how this penalty term is related to both regularized
auto-encoders and denoising encoders and how it can be seen as a link between
deterministic and non-deterministic auto-encoders. We find empirically that
this penalty helps to carve a representation that better captures the local
directions of variation dictated by the data, corresponding to a
lower-dimensional non-linear manifold, while being more invariant to the vast
majority of directions orthogonal to the manifold. Finally, we show that by
using the learned features to initialize a MLP, we achieve state of the art
classification error on a range of datasets, surpassing other methods of
pre-training.
