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Title: Deep Boosting of Diverse Experts

Paper content:
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.
The rapid growth of computational powers of GPUs has provided good opportunities for us to develop scalable learning algorithms to leverage massive digital images to train more discriminative classifiers for large-scale visual recognition applications, and deep learning BID19 BID20 BID3 has demonstrated its outstanding performance because highly invariant and discriminant features and multi-way softmax classifier are learned jointly in an end-to-end fashion.Before deep learning becomes so popular, boosting has achieved good success on visual recognition BID21 .
By embedding multiple weak learners to construct an ensemble one, boosting BID15 can significantly improve the performance by sequentially training multiple weak learners with respect to a weighted error function which assigns larger weights to the samples misclassified by the previous weak learners.
Thus it is very attractive to invest whether boosting can be integrated with deep learning to achieve higher accuracy rates on large-scale visual recognition.By using neural networks to replace the traditional weak learners in the boosting frameworks, boosting of neural networks has received enough attentions BID23 BID10 BID7 BID9 .
All these existing deep boosting algorithms simply use the weighted error function (proposed by Adaboost (Schapire, 1999) ) to replace the softmax error function (used in deep learning ) that treats all the errors equally.
Because different object classes may have different learning complexities, it is more attractive to invest new deep boosting algorithm that can use different weights over various object classes rather than over different training samples.Motivated by this observation, a deep boosting algorithm is developed to generate more discriminative ensemble classifier by combining a set of base deep CNNs with diverse capabilities, e.g., all these base deep CNNs (base experts) are sequentially trained to recognize different subsets of object classes in an easy-to-hard way according to their learning complexities.
The rest of the paper is organized as: Section 2 briefly reviews the related work; Section 3 introduce our deep boosting algorithm; Section 4 reports our experimental results; and we conclude this paper at Section 5.
In this paper, we develop a deep boosting algorithm is to learn more discriminative ensemble classifier by combining a set of base experts with diverse capabilities.
The base experts are from the family of deep CNNs and they are sequentially trained to recognize a set of object classes in an easy-to-hard way according to their learning complexities.
As for the future network, we would like to investigate the performance of heterogeneous base deep networks from different families.

assistant: text: A deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs.