Notes
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. We explore various types of generative models, with a thorough analysis of their respective limitations and potential applications. Additionally, we also investigate several influence methods / instance-based interpretation and explore their efficacy in relevant applications.
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Generative Models
- Variational Autoencoders (VAE)
- Generative Adversarial Networks (GAN)
- Normalizing Flows
- Diffusion Models
Influence Methods
- TracIn
- Influence Functions
- Representer Points
- (Application: Memorization in Generative Models)