SAM: A Rethinking of Prominent Convolutional Neural Network Architectures for Visual Object Recognition

Convolutional neural networks play an increasingly important role in computer vision tasks, especially in the field of visual object recognition. Many prominent models, such as Inception, Maxout, ResNet, and NIN, have been proposed to significantly improve recognition performance. Inspired from those models, we propose a novel module called self-adaptive module (SAM). SAM consists of four passes and one selector. Specifically, the four passes include two direct passes with different receptive fields and depths, one residual pass, and one Maxout pass. Actually, the residual pass is used to speed up convergence, while we take advantage of the Maxout pass to enhance approximate capabilities of SAM. The selector is further designed to help choose reasonable output. Basically, SAM is intended to simplify design of any new deep learning architecture, since it no longer requires consideration of how to select receptive fields and depths. Our SAM is tested on the visual object recognition datasets including CIFAR-10, CIFAR-100, MNIST, and SVHN. The experimental results demonstrate that the SAM-Net has superior recognition performances on the four benchmarks, which achieve test errors of 5.76%, 28.56%, 0.31%, and 1.98%, respectively.