Guided Learning for the combination of weakly supervised and semi supervised learning
We propose a simple but efficient method to combine semi supervised learning with weakly supervised learning for deep neural networks. Weakly supervised learning is to solve the task which requires fine level prediction with only coarse level annotations available. Designing deep neural networks for weakly supervised learning is always accompanied by a trade off between fine level information detection performance and coarse level classification accuracy. While combining weakly supervised learning with semi supervised learning using unlabeled data, in contrast to seeking for this trade off, we design two different models for different targets. One merely pursues finer information detection performance as the final target, while another one is more professional in achieving 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 Guided Learning for these two different models to improve the training efficiency. Our approach outperforms the first place result on DCASE2018 Task 4 which employs Mean Teacher with a well design CRNN network from 32.4% to 38.9% achieving state of the art performance.