Diversity encouraging ensemble of convolutional networks for high performance action recognition
We present a simple and effective ensemble method, Diversity Encouraging Ensemble (DEE), for deep convolutional networks to boost their performances. By training the convolutional network in two stages, we generate multiple component networks without adding any training cost. On the one hand, we modify the structure parameters of component networks in the training process to enlarge the diversities of the networks, which is found to be beneficial to improving the ensemble performance. On the other hand, we exploit monotonous decreasing learning rate schedule to accelerate the speed of deep network converging to different local minima, and we decrease the training time of integrating multiple networks to that of training a single network from traditional multi-step learning policy. We evaluate our ensemble method on two challenging action datasets, UCF-101 and HMDB-51, and obtain performance improvements from single deep network and other ensemble methods. Our results also outperform many state-of-the-art action recognition methods.