An effective weighting scheme for training samples is essential for learning tasks. Numerous weighting schemes have been proposed. Some schemes take the easy-first mode on samples, whereas some others take the hard-first mode. Naturally, an interesting yet realistic question is raised. Which samples should be learned first given a new learning task, easy or hard? To answer this question, three aspects of research are carried out. First, a high-level unified weighted loss is proposed, providing a more comprehensive view for existing schemes. Theoretical analysis is subsequently conducted and preliminary conclusions are obtained. Second, a flexible weighting scheme is proposed to overcome the defects of existing schemes. The three modes, namely, easy/medium/hard-first, can be flexibly switched in the proposed scheme. Third, a wide range of experiments are conducted to further compare the weighting schemes in different modes. On the basis of these works, reasonable answers are obtained. Factors including prior knowledge and data characteristics determine which samples should be learned first in a learning task.
翻译:培训样本的有效加权办法对于学习任务至关重要,已经提出了许多加权办法,有些方案在抽样方面采用简单第一模式,而另一些方案则采用硬第一模式。自然,提出一个有趣但现实的问题。首先应当学习哪些样本,而新的学习任务是容易的还是困难的?为了回答这个问题,已经进行了三个方面的研究。首先,提出了高层次的统一加权损失,为现有计划提供了更全面的观点。随后进行了理论分析,并取得了初步结论。第二,提出了灵活的加权办法,以克服现有计划的缺陷。三种模式,即简单/中/硬第一模式,可以在拟议办法中灵活地转换。第三,进行了广泛的实验,进一步比较不同模式的加权办法。根据这些工作,获得了合理的答案。包括先前的知识和数据特点在内的因素决定了哪些样本应首先在学习任务中学习。