  Semi-supervised learning approaches have emerged as an active area of
research to combat the challenge of obtaining large amounts of annotated data.
Towards the goal of improving the performance of semi-supervised learning
methods, we propose a novel framework, HIERMATCH, a semi-supervised approach
that leverages hierarchical information to reduce labeling costs and performs
as well as a vanilla semi-supervised learning method. Hierarchical information
is often available as prior knowledge in the form of coarse labels (e.g.,
woodpeckers) for images with fine-grained labels (e.g., downy woodpeckers or
golden-fronted woodpeckers). However, the use of supervision using coarse
category labels to improve semi-supervised techniques has not been explored. In
the absence of fine-grained labels, HIERMATCH exploits the label hierarchy and
uses coarse class labels as a weak supervisory signal. Additionally, HIERMATCH
is a generic-approach to improve any semisupervised learning framework, we
demonstrate this using our results on recent state-of-the-art techniques
MixMatch and FixMatch. We evaluate the efficacy of HIERMATCH on two benchmark
datasets, namely CIFAR-100 and NABirds. HIERMATCH can reduce the usage of
fine-grained labels by 50% on CIFAR-100 with only a marginal drop of 0.59% in
top-1 accuracy as compared to MixMatch. Code:
https://github.com/07Agarg/HIERMATCH
