  An iterative method of learning has become a paradigm for training deep
convolutional neural networks (DCNN). However, utilizing a non-iterative
learning strategy can accelerate the training process of the DCNN and
surprisingly such approach has been rarely explored by the deep learning (DL)
community. It motivates this paper to introduce a non-iterative learning
strategy that eliminates the backpropagation (BP) at the top dense or fully
connected (FC) layers of DCNN, resulting in, lower training time and higher
performance. The proposed method exploits the Moore-Penrose Inverse to pull
back the current residual error to each FC layer, generating well-generalized
features. Then using the recomputed features, i.e., the new generalized
features the weights of each FC layer is computed according to the
Moore-Penrose Inverse. We evaluate the proposed approach on six widely accepted
object recognition benchmark datasets: Scene-15, CIFAR-10, CIFAR-100, SUN-397,
Places365, and ImageNet. The experimental results show that the proposed method
obtains significant improvements over 30 state-of-the-art methods.
Interestingly, it also indicates that any DCNN with the proposed method can
provide better performance than the same network with its original training
based on BP.
