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Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images

2022-11-22 06:42:41
Alexander Ororbia, Ankur Mali

Abstract

In this work, we develop convolutional neural generative coding (Conv-NGC), a generalization of predictive coding to the case of convolution/deconvolution-based computation. Specifically, we concretely implement a flexible neurobiologically-motivated algorithm that progressively refines latent state maps in order to dynamically form a more accurate internal representation/reconstruction model of natural images. The performance of the resulting sensory processing system is evaluated on several benchmark datasets such as Color-MNIST, CIFAR-10, and Street House View Numbers (SVHN). We study the effectiveness of our brain-inspired neural system on the tasks of reconstruction and image denoising and find that it is competitive with convolutional auto-encoding systems trained by backpropagation of errors and notably outperforms them with respect to out-of-distribution reconstruction (including on the full 90k CINIC-10 test set).

Abstract (translated)

URL

https://arxiv.org/abs/2211.12047

PDF

https://arxiv.org/pdf/2211.12047.pdf


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