  Startups often represent newly established business models associated with
disruptive innovation and high scalability. They are commonly regarded as
powerful engines for economic and social development. Meanwhile, startups are
heavily constrained by many factors such as limited financial funding and human
resources. Therefore, the chance for a startup to eventually succeed is as rare
as "spotting a unicorn in the wild". Venture Capital (VC) strives to identify
and invest in unicorn startups during their early stages, hoping to gain a high
return. To avoid entirely relying on human domain expertise and intuition,
investors usually employ data-driven approaches to forecast the success
probability of startups. Over the past two decades, the industry has gone
through a paradigm shift moving from conventional statistical approaches
towards becoming machine-learning (ML) based. Notably, the rapid growth of data
volume and variety is quickly ushering in deep learning (DL), a subset of ML,
as a potentially superior approach in terms of capacity and expressivity. In
this work, we carry out a literature review and synthesis on DL-based
approaches, covering the entire DL life cycle. The objective is a) to obtain a
thorough and in-depth understanding of the methodologies for startup evaluation
using DL, and b) to distil valuable and actionable learning for practitioners.
To the best of our knowledge, our work is the first of this kind.
