  State-of-the-art deep learning models are often trained with a large amount
of costly labeled training data. However, requiring exhaustive manual
annotations may degrade the model's generalizability in the limited-label
regime. Semi-supervised learning and unsupervised learning offer promising
paradigms to learn from an abundance of unlabeled visual data. Recent progress
in these paradigms has indicated the strong benefits of leveraging unlabeled
data to improve model generalization and provide better model initialization.
In this survey, we review the recent advanced deep learning algorithms on
semi-supervised learning (SSL) and unsupervised learning (UL) for visual
recognition from a unified perspective. To offer a holistic understanding of
the state-of-the-art in these areas, we propose a unified taxonomy. We
categorize existing representative SSL and UL with comprehensive and insightful
analysis to highlight their design rationales in different learning scenarios
and applications in different computer vision tasks. Lastly, we discuss the
emerging trends and open challenges in SSL and UL to shed light on future
critical research directions.
