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Deep learning based approaches have achieved significant progresses indifferent tasks like classification, detection, segmentation, and so on.Ensemble learning is widely known to further improve performance by combiningmultiple complementary models. It is easy to apply ensemble learning forclassification tasks, for example, based on averaging, voting, or othermethods. However, for other tasks (like object detection) where the outputs arevarying in quantity and unable to be simply compared, the ensemble of multiplemodels become difficult. In this paper, we propose a new method calledPredictive Ensemble Learning (PEL), based on powerful predictive ability ofdeep neural networks, to directly predict the best performing model among apool of base models for each test example, thus transforming ensemble learningto a traditional classification task. Taking scene text detection as theapplication, where no suitable ensemble learning strategy exists, PEL cansignificantly improve the performance, compared to either individualstate-of-the-art models, or the fusion of multiple models by non-maximumsuppression. Experimental results show the possibility and potential of PEL inpredicting different models' performance based only on a query example, whichcan be extended for ensemble learning in many other complex tasks.

Predictive Ensemble Learning with Application to Scene Text Detection