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The field of machine learning has witnessed a surge in research on generative models, as these models possess the ability to generate novel data similar to the training data. In this study, our primary objective is to explore various types of generative models, with a thorough analysis of their respective limitations and potential applications. Additionally, we will investigate several influence methods and explore their efficacy in addressing the issue of memorization in generative models.
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Generative Models
- Variational Autoencoders (VAE)
- Generative Adversarial Networks (GAN)
- Normalizing Flows
- Diffusion Models
Influence Methods
- TracIn
- Influence Functions
- Representer Points