  Learning from imprecise labels such as "animal" or "bird", but making precise
predictions like "snow bunting" at test time is an important capability 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 is not capable of learning from effectively
unlabeled examples at 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 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 and
show 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.
