METHOD FOR RANDOM SAMPLED CONVOLUTIONS WITH LOW COST ENHANCED EXPRESSIVE POWER

摘要

A system and method for random sampled convolutions are disclosed to efficiently boost a convolutional neural network (CNN) expressive power without adding computation cost. The method for random sampled convolutions selects a receptive field size and generates filters with a subset of the receptive field elements, the number of learnable parameters, as being active, wherein the number learnable parameters corresponds to computing characteristics, such as SIMD capability, of the processing system upon which the CNN is executed. Several random filters may be generated, with each being run separately on the CNN. The random filter that causes the fastest convergence is selected over the others. The placement of the random filter in the CNN may be per layer, per channel, or per convergence operation. The CNN employing the random sampled convolutions method performs as well as other CNNs utilizing the same receptive field size.