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Pair wise training for stacked convolutional autoencoders using small scale images

Publication Type : Journal Article

Publisher : IOS Press

Source : Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 1987–1995, Mar. 2019, doi: 10.3233/jifs-169910.

Url : https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169910

Campus : Amritapuri

School : Department of Computer Science and Engineering, School of Computing, School of Engineering

Center : Computer Vision and Robotics

Department : Computer Science

Verified : No

Year : 2019

Abstract : Deep neural networks have dramatically gained immense potential in almost every field like computer vision, natural language processing, biomedical informatics etc. Among these networks, autoencoders are popular in performing dimensionality reduction task, while learning a representation for an unlabeled dataset. A usual way of dealing with such networks is to pre-train them in a layer-wise fashion, and consequently fine-tune the whole stack in a supervised manner. In this paper, a pair-wise training strategy is proposed to determine optimum model parameters by reducing training time as well as the complexity of training a convolutional autoencoder without compromising its accuracy. The proposed approach works in a fully unsupervised manner and has been tested on datasets like MNIST, CIFAR10, and CIFAR100 and it shows that the training time has improved by an average of 25% on these three datasets. © 2019 - IOS Press and the authors.

Cite this Research Publication : A. S. Kumar and J. J. Nair, "Pair wise training for stacked convolutional autoencoders using small scale images," Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 1987–1995, Mar. 2019, doi: 10.3233/jifs-169910.

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