What you need is a more professional teacher
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We propose a simple and efficient method to combine semi-supervised learning with weakly-supervised learning for deep neural networks. Designing deep neural networks for weakly-supervised learning is always accompanied by a tradeoff between fine-information and coarse-level classification accuracy. While using unlabeled data for semi-supervised learning, in contrast to seeking for this tradeoff, we design two extremely different models for different targets, one of which just pursues finer information for the final target. Another one is more professional to achieve higher coarse-level classification accuracy so that it is regarded as a more professional teacher to teach the former model using unlabeled data. We present an end-to-end semi-supervised learning process termed guiding learning for these two different models so that improve the training efficiency. Our approach improves the 1^st place result on Task4 of the DCASE2018 challenge from 32.4% to 38.3%, achieving start-of-art performance.
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