Explicit guiding auto-encoders for learning meaningful representation

Sun, Yanan, Mao, Hua, Sang, Yongsheng and Yi, Zhang (2017) Explicit guiding auto-encoders for learning meaningful representation. Neural Computing and Applications, 28 (3). pp. 429-436. ISSN 0941-0643

Sun et al - Explicit guiding auto-encoders for learning meaningful representation AAM.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1007/s00521-015-2082-x


The auto-encoder model plays a crucial role in the success of deep learning. During the pre-training phase, auto-encoders learn a representation that helps improve the performance of the entire neural network during the fine-tuning phase of deep learning. However, the learned representation is not always meaningful and the network does not necessarily achieve higher performance with such representation because auto-encoders are trained in an unsupervised manner without knowing the specific task targeted in the fine-tuning phase. In this paper, we propose a novel approach to train auto-encoders by adding an explicit guiding term to the traditional reconstruction cost function that encourages the auto-encoder to learn meaningful features. Particularly, the guiding term is the classification error with respect to the representation learned by the auto-encoder, and a meaningful representation means that a network using the representation as input has a low classification error in a classification task. In our experiments, we show that the additional explicit guiding term helps the auto-encoder understand the prospective target in advance. During learning, it can drive the learning toward a minimum with better generalization with respect to the particular supervised task on the dataset. Over a range of image classification benchmarks, we achieve equal or superior results to baseline auto-encoders with the same configuration.

Item Type: Article
Uncontrolled Keywords: Auto-encoders, Deep learning, Representation learning, Neural network
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 13 Jun 2019 11:57
Last Modified: 01 Aug 2021 11:30
URI: http://nrl.northumbria.ac.uk/id/eprint/39672

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