On Approximation in Deep Convolutional Networks: a Kernel Perspective
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The success of deep convolutional networks on on tasks involving high-dimensional data such as images or audio suggests that they are able to efficiently approximate certain classes of functions that are not cursed by dimensionality. In this paper, we study this theoretically and empirically through the lens of kernel methods, by considering multi-layer convolutional kernels, which have achieved good empirical performance on standard vision datasets, and provide theoretical descriptions of over-parameterized convolutional networks in certain regimes. We find that while expressive kernels operating on input patches are important at the first layer, simpler polynomial kernels can suffice in higher layers for good performance. For such simplified models, we provide a precise functional description of the RKHS and its regularization properties, highlighting the role of depth for capturing interactions between different parts of the input signal, and the role of pooling for encouraging smooth dependence on the global or relative positions of such parts.
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