  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.
