Retracted : Optimizing the convolutional network layers using the Viola–Jones framework and ternary weight networks

Abstract

In this paper, we have presented a method of combining the weight optimizations from ternary weight networks with a set of custom features inspired by the Viola–Jones Object Detection Framework to improve a network's performance. By replacing the first convolutional layer in a network with a function using a custom set of filters, we can reduce the number of operations required to compute the layer's outputs. These filters are composed of four rectangles, labeled as either positive or negative, within the filter space. When combined with an integral image, these filters allow for more efficient computations of feature values than a standard convolutional layer. When using our custom filters of shape 5 × 5, the layer only requires 64% of the operations that a standard convolutional layer need. This reduction only increases as the filter sizes get larger. Empirically, we have found that these layers' real‐world execution times behave as theoretically calculated and that their optimizations do not lead to significant drop inaccuracy. During testing, we evaluated our proposed optimization on two different data sets with two different network architectures.