Title: Deep Boosting of Diverse Experts. Abstract: 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.