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Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full-connection, the application of auto-encoders is usually limited to small, well aligned images. In this paper, we incorporate the supervised information to propose a novel formulation, namely class-encoder, whose training objective is to reconstruct a sample from another one ofdoi:10.1109/cvprw.2016.143 dblp:conf/cvpr/ShiZLLL16 fatcat:kqf2v7e6jnfrbp6pon33cqclqi