Coupled-learning convolutional neural networks for object recognition

Abstract

Recently, convolutional neural networks (CNN) have been attracting considerable attention in various computer vision tasks. Motivated by neuroscience, CNN has several similar properties with the learning process of human brain. A prominent difference is that each CNN is an independent learning process while the effective interaction/communication between people can play important role in the human visual system. Inspired by this fact, we proposed a novel Coupled-learning Convolutional Neural Network (Co-CNN) for the task of object recognition, which boosts its discriminative capability by employing the dynamic interaction between neural networks. Contrary to existing network architectures posing the network optimization problem as an isolated learning process, the intuition behind the Co-CNN framework is that the coupled learning mechanism may prevent the algorithm away from over-fitting to one or more particular objective functions. The proposed Co-CNN framework has three unique characteristics: (1) Co-CNN, which is a novel deep network learning framework, can simultaneously optimize both neural networks with same/different structures. (2) The learned semantic information, which can be gradually mined from neural networks, is employed to guide the communication between neural networks. (3) Co-CNN well incorporates the coupled-learning mechanism into the process of learning neural networks, and then further improve the recognition performance of neural networks by adopting the learned semantic information. Comprehensive evaluations on five benchmark datasets (CIFAR-10, CIFAR-100, MNIST, SVHN and Imagenet) well demonstrate the significant superiority of our proposed Co-CNN framework over other existing algorithms.