  The recently proposed Mean Teacher method, which exploits large-scale
unlabeled data in a self-ensembling manner, has achieved state-of-the-art
results in several semi-supervised learning benchmarks. Spurred by current
achievements, this paper proposes an effective Couple Learning method that
combines a well-trained model and a Mean Teacher model. The suggested
pseudo-labels generated model (PLG) increases strongly- and weakly-labeled data
to improve the Mean Teacher method-s performance. Moreover, the Mean Teacher-s
consistency cost reduces the noise impact in the pseudo-labels introduced by
detection errors. The experimental results on Task 4 of the DCASE2020 challenge
demonstrate the superiority of the proposed method, achieving about 44.25%
F1-score on the public evaluation set, significantly outperforming the baseline
system-s 32.39%. At the same time, we also propose a simple and effective
experiment called the Variable Order Input (VOI) experiment, which proves the
significance of the Couple Learning method. Our developed Couple Learning code
is available on GitHub.
