A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot

• URL: http://arxiv.org/abs/2307.14397v1
• Date: Wed, 26 Jul 2023 12:05:08 GMT
• Title: A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
• Authors: Milad Abdollahzadeh, Touba Malekzadeh, Christopher T. H. Teo, Keshigeyan Chandrasegaran, Guimeng Liu, Ngai-Man Cheung
• Abstract summary: In machine learning, generative modeling aims to generate new data statistically similar to the training data distribution. This is an important topic when data acquisition is challenging, e.g. healthcare applications. We study interactions between different GM-DC tasks and approaches.
• Score: 33.564516823250806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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