Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce. Contributions by volunteers or results of web crawling lack precision in this manner, but are still valuable. And crucially, these weakly labeled examples are available in larger quantities for lower cost than high-quality bespoke training data. CHILLAX, a recently proposed method to tackle this task, leverages a hierarchical classifier to learn from imprecise labels. However, it has two major limitations. First, it does not learn from examples labeled as the root of the hierarchy, e.g., "object". Second, an extrapolation of annotations to precise labels is only performed at test time, where confident extrapolations could be already used as training data. In this work, we extend CHILLAX with a self-supervised scheme using constrained semantic extrapolation to generate pseudo-labels. This addresses the second concern, which in turn solves the first problem, enabling an even weaker supervision requirement than CHILLAX. We evaluate our approach empirically, showing that our method allows for a consistent accuracy improvement of 0.84 to 1.19 percent points over CHILLAX and is suitable as a drop-in replacement without any negative consequences such as longer training times.
翻译:从诸如“动物”或“鸟类”等不精确的标签中学习,但是在推论时间作出精确的预测,对于任何分类者来说,当专家标签的培训数据很少的时候,“现在的捆绑”是一个重要的能力。 志愿者的贡献或网络爬动的结果缺乏这种方式的精确性,但仍然很宝贵。 最重要的是,这些标签薄弱的例子在数量上比高质量的培训数据要低,成本要低得多。 CHILLLAX是最近提出的一项处理这项任务的方法,它利用一个等级分类器从不精确的标签中学习。 但是,它有两个主要的局限性。 首先,它没有从被标为等级之根的例子中学习,例如“ 目标 ” 。 第二, 精确标签的注释的外推法只能在测试时间进行, 而在测试时间里, 信心外推法已经被用来作为培训数据。 在这项工作中, 我们用一个自我监督的办法推广 CHILLAX, 使用有限的外推法来生成假标签。 这解决了第二个问题, 这个问题反过来解决了第一个问题, 也就是解决了问题, 使更精确的监管更合适的方式成为了我们1ILAX 的正确性的方法, 。