The recently proposed Mean Teacher has achieved state-of-the-art results in several semi-supervised learning benchmarks. The Mean Teacher method can exploit large-scale unlabeled data in a self-ensembling manner. In this paper, an effective Couple Learning method based on a well-trained model and a Mean Teacher model is proposed. The proposed pseudo-labels generated model (PLG) can increase strongly-labeled data and weakly-labeled data to improve performance of the Mean Teacher method. The Mean Teacher method can suppress noise in pseudo-labels data. The Couple Learning method can extract more information in the compound training data. These experimental results on Task 4 of the DCASE2020 challenge demonstrate the superiority of the proposed method, achieving about 39.18% F1-score on public eval set, outperforming 37.12% of the baseline system by a significant margin.
翻译:最近提出的 " 良师 " 方案在一些半监督的学习基准中取得了最新成果。 " 良师 " 方案可以以自我组合的方式利用大规模无标签数据。在本文中,提出了一种基于良好培训模式和 " 良师 " 模式的有效 " 方案。 拟议的假标签生成模式(PLG)可以增加贴有强烈标签的数据和标签不高的数据,以提高 " 良师 " 方法的绩效。 " 优师 " 方案可以抑制假标签数据中的噪音。 " 双师 " 方案可以在复合培训数据中提取更多信息。 " 双师 " 方案 " 挑战 " 任务4 " 的实验结果显示了拟议方法的优越性,在公共电子价值集上实现了大约39.18%的F1分数,大大超过基线系统的37.12%。