Auto-encoder is a special kind of neural network based on reconstruction.
De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to
the input by corrupting the original data first and then reconstructing the
original input by minimizing the reconstruction error function. And contractive
auto-encoder (CAE) is another kind of improved auto-encoder to learn robust
feature by introducing the Frobenius norm of the Jacobean matrix of the learned
feature with respect to the original input. In this paper, we combine
de-noising auto-encoder and contractive auto- encoder, and propose another
improved auto-encoder, contractive de-noising auto- encoder (CDAE), which is
robust to both the original input and the learned feature. We stack CDAE to
extract more abstract features and apply SVM for classification. The experiment
result on benchmark dataset MNIST shows that our proposed CDAE performed better
than both DAE and CAE, proving the effective of our method.Comment: Figures edite