In this paper, a deep boosting algorithm is developed to
 learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts)
 with diverse capabilities, e.g., these base deep CNNs are
 sequentially trained to recognize a set of 
 object classes in an easy-to-hard way according to their
 learning complexities. Our experimental results have demonstrated
 that our deep boosting algorithm can significantly improve the
 accuracy rates on large-scale visual recognition.
 A deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs.