Semi-Random Deep Neural Networks for Near Real-Time Target Classification

In recent years deep neural networks have shown great advances in image processing tasks. For modern datasets, these networks require long training times due to backpropagation, high amount of computational resources for weight updates, and memory intensive weight storage. Exploiting randomness during the training of deep neural networks can mitigate these concerns by reducing the computational costs without sacrificing network performance. However, a fully randomized network has limitations for real-time target classification as it leads to poor performance. Therefore we are motivated in using semi-random deep neural networks to exploit random fixed weights. In this paper, we demonstrate that semi-random deep neural networks can achieve near real-time training with comparable accuracies to conventional deep neural networks models. We find that these networks are enhanced by the usage of skip connections and train rapidly at the cost of dense memory usage. With greater memory resources available, these networks can train on larger datasets at a fraction of the training time costs. These semi-random deep neural network architectures open up an avenue for further research in utilizing random fixed weights in neural networks.