  We propose a novel network initialization method using Perlin noise for
training image classification networks with a limited amount of data. Our main
idea is to initialize the network parameters by solving an artificial noise
classification problem, where the aim is to classify Perlin noise samples into
their noise categories. Specifically, the proposed method consists of two
steps. First, it generates Perlin noise samples with category labels defined
based on noise complexity. Second, it solves a classification problem, in which
network parameters are optimized to classify the generated noise samples. This
method produces a reasonable set of initial weights (filters) for image
classification. To the best of our knowledge, this is the first work to
initialize networks by solving an artificial optimization problem without using
any real-world images. Our experiments show that the proposed method
outperforms conventional initialization methods on four image classification
datasets.
