A Survey on Generative Modeling with Limited Data, Few Shots, and Zero
  Shot
Milad Abdollahzadeh Touba Malekzadeh Christopher T. H. Teo Keshigeyan Chandrasegaran Guimeng Liu Ngai-Man Cheung
In machine learning, generative modeling aims to learn to generate new data
statistically similar to the training data distribution. In this paper, we
survey learning generative models under limited data, few shots and zero shot,
referred to as Generative Modeling under Data Constraint (GM-DC). This is an
important topic when data acquisition is challenging, e.g. healthcare
applications. We discuss background, challenges, and propose two taxonomies:
one on GM-DC tasks and another on GM-DC approaches. Importantly, we study
interactions between different GM-DC tasks and approaches. Furthermore, we
highlight research gaps, research trends, and potential avenues for future
exploration. Project website: https://gmdc-survey.github.io.