Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization

Meshia Cédric Oveneke; Mitchel Aliosha-Perez; Yong Zhao; Dongmei Jiang; Hichem Sahli

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arXiv · 1611.09232

Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization

Meshia Cédric Oveneke·Mitchel Aliosha-Perez·Yong Zhao·Dongmei Jiang·Hichem Sahli

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Abstract

The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and specialized hardware. Despite the indisputable success, the reliance on huge amounts of labeled data and specialized hardware can be a limiting factor when approaching new applications. To help alleviating these limitations, we propose an efficient learning strategy for layer-wise unsupervised training of deep CNNs on conventional hardware in acceptable time. Our proposed strategy consists of randomly convexifying the reconstruction contractive auto-encoding (RCAE) learning objective and solving the resulting large-scale convex minimization problem in the frequency domain via coordinate descent (CD). The main advantages of our proposed learning strategy are: (1) single tunable optimization parameter; (2) fast and guaranteed convergence; (3) possibilities for full parallelization. Numerical experiments show that our proposed learning strategy scales (in the worst case) linearly with image size, number of filters and filter size.

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Meshia Cédric Oveneke, Mitchel Aliosha-Perez, Yong Zhao, Dongmei Jiang, Hichem Sahli. 2016-11-28. Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization. https://arxiv.org/abs/1611.09232

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