We present a novel convolutional auto-encoder (CAE) for unsupervised feature learning. A stack of CAEs forms a convolutional neural network (CNN). Each CAE is trained using conventional on-line gradient descent without additional regularization terms. A max-pooling layer is essential to learn biologically plausible features consistent with those found by previous approaches. Initializing a CNN with filters of a trained CAE stack yields superior performance on a digit (MNIST) and an object recognition (CIFAR10) benchmark.
['Jonathan Masci', 'Ueli Meier', 'Dan C. Ciresan', 'Jürgen Schmidhuber']
Stacked convolutional auto-encoders for hierarchical feature extraction