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.