Fast Inference Predictive Coding: A Novel Model for Constructing Deep Neural Networks

doi:10.1109/TNNLS.2018.2862866

Fast Inference Predictive Coding: A Novel Model for Constructing Deep Neural Networks

2019-04-01

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DOI：10.1109/TNNLS.2018.2862866

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As a biomimetic model of visual information processing, predictive coding (PC) has become increasingly popular for explaining a range of neural responses and many aspects of brain organization. While the development of PC model is encouraging in the neurobiology community, its practical applications in machine learning (e.g., image classification) have not been fully explored yet. In this paper, a novel image processing model called fast inference PC (FIPC) is presented for image representation and classification. Compared with the basic PC model, a regression procedure and a classification layer have been added to the proposed FIPC model. The regression procedure is used to learn regression mappings that achieve fast inference at test time, while the classification layer can instruct the model to extract more discriminative features. In addition, effective learning and fine-tuning algorithms are developed for the proposed model. Experimental results obtained on four image benchmark data sets show that our model is able to directly and fast infer representations and, simultaneously, produce lower error rates on image classification tasks.

Keywords:

Deep learning (DL)

feature extraction

image classification

inference mechanisms

predictive coding (PC)

Fast Inference Predictive Coding: A Novel Model for Constructing Deep Neural Networks

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