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Lecture Notes in Computer Science
In this paper, we propose a novel autoencoder variant, smooth autoencoder (SmAE), to learn robust and discriminative feature representations. Different from conventional autoencoders which reconstruct each sample from its encoding, we use the encoding of each sample to reconstruct its local neighbors. In this way, the learned representations are consistent among local neighbors and robust to small variations of the inputs. When trained with supervisory information, our approach forces samplesdoi:10.1007/978-3-319-16808-1_6 fatcat:ca5ydjwn4bfiznx6anghvr62qi