This is a contributed article written by Professor Lim Seong-bin of Korea University.
Summation
Advances in Generative AI: Generative AI is advancing rapidly and can generate high-quality data in various forms, such as video, text, and audio.
Principle of Generative Models: Generative models are defined as sampling unobserved data from a probability distribution, and to do this, you must understand the concepts of probability distribution and sampling.
GAN and VAE: GAN is based on game theory between a generator and a discriminator, while VAE uses a method of compressing and restoring the probability distribution of the latent space.
Diffusion Model: Diffusion Model requires mathematical foundations such as probability theory and stochastic differential equations, and its main goal is to find a function that maps from noise space to data space.
DAE(Denoising AutoEncoder): DAE learns how to restore the original data after adding noise to the data, and is useful for extracting robust patterns.
Questions to consider
The Future of Generative AI: Based on the current pace of development and technology of generative AI, can we predict what areas it might be used in in the future?
Application of Diffusion Model: Considering the mathematical complexity of Diffusion Model, what are the advantages and disadvantages of applying it to real industries?
Data quality and diversity: How do technologies such as GANs, VAEs, and Diffusion Models ensure data quality and diversity, and how can these characteristics be evaluated?
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