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We introduce a parameter sharing scheme, in which different layers of aconvolutional neural network (CNN) are defined by a learned linear combinationof parameter tensors from a global bank of templates. Restricting the number oftemplates yields a flexible hybridization of traditional CNNs and recurrentnetworks. Compared to traditional CNNs, we demonstrate substantial parametersavings on standard image classification tasks, while maintaining accuracy.  Our simple parameter sharing scheme, though defined via soft weights, inpractice often yields trained networks with near strict recurrent structure;with negligible side effects, they convert into networks with actual loops.Training these networks thus implicitly involves discovery of suitablerecurrent architectures. Though considering only the design aspect of recurrentlinks, our trained networks achieve accuracy competitive with those built usingstate-of-the-art neural architecture search (NAS) procedures.  Our hybridization of recurrent and convolutional networks may also representa beneficial architectural bias. Specifically, on synthetic tasks which arealgorithmic in nature, our hybrid networks both train faster and extrapolatebetter to test examples outside the span of the training set.

Learning Implicitly Recurrent CNNs Through Parameter Sharing