Learning Deep Representations Using Convolutional Auto-Encoders with Symmetric Skip Connections

Convolutional neural networks (CNNs) have shown their power on many computer vision tasks. However, there are still some limitations, including their sensitivity to weight initialization and dependency to large scale labeled data. In this paper, we try to address these two problems by proposing a simple yet powerful CNN based denoising auto-encoder network which can be trained end-to-end in an unsupervised manner. The network architecture we use is a fully convolutional auto-encoder with symmetric encoder-decoder connections. The proposed method can not only reconstruct clean images from corrupted ones, but also learn abstract image representation through the reconstruction training. The encoder part of the network can be a good all-convolution network for classification. With the help of unsupervised pre-training, it achieves very competitive results even without extra unlabeled data. Further more, we show experimentally that our network also performs well in semi -supervised learning tasks.