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.
authors: Wei Zhang Qiuyu Chen Jun Yu Jianping Fan
hIndex: Jianping Fan 39