Flow-based Deep Generative Models
📰 Lilian Weng's Blog
Flow-based generative models learn the probability density function of input data explicitly, differing from GAN and VAE
Action Steps
- Learn the basics of generative models, including GAN and VAE
- Understand the concept of probability density functions and their role in generative models
- Explore the architecture and training process of flow-based generative models
- Apply flow-based models to real-world data generation tasks
Who Needs to Know This
Data scientists and AI engineers can benefit from understanding flow-based models for generative tasks, as they provide an alternative approach to GAN and VAE
Key Insight
💡 Flow-based models provide an explicit and tractable way to learn probability density functions, differing from implicit models like GAN
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🤖 Flow-based generative models explicitly learn probability density functions! 📊
Key Takeaways
Flow-based generative models learn the probability density function of input data explicitly, differing from GAN and VAE
Full Article
<!-- In this post, we are looking into the third type of generative models: flow-based generative models. Different from GAN and VAE, they explicitly learn the probability density function of the input data. --> <p>So far, I’ve written about two types of generative models, <a href="https://lilianweng.github.io/posts/2017-08-20-gan/">GAN</a> and <a href="https://lilianweng.github.io/posts/2018-08-12-vae/">VAE</a>. Neither of them explicitly learns the probability density function of real da
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