ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks
نویسندگان
چکیده
We propose to learn a curriculum or a syllabus for supervised learning with deep neural networks. Specifically, we learn weights for each sample in training by an attached neural network, called ScreenerNet, to the original network and jointly train them in an end-to-end fashion. We show the networks augmented with our ScreenerNet achieve early convergence with better accuracy than the state-of-the-art rule-based curricular learning methods in extensive experiments using three popular vision datasets including MNIST, CIFAR10 and Pascal VOC2012, and a Cartpole task using Deep Q-learning.
منابع مشابه
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[1] Bengio, Yoshua, Louradour, Jérôme, Collobert, Ronan, and Weston, Jason. Curriculum learning. In ICML , 2009. [2] Kumar, M Pawan, Packer, Benjamin, and Koller, Daphne. Self-paced learning for latent variable models. In NIPS , 2010. [3] Shrivastava, Abhinav, Gupta, Abhinav, and Girshick, Ross. Training regionbased object detectors with online hard example mining. In CVPR , 2016. [4] Avramova,...
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