In the ever-evolving realm of machine learning, generative models have emerged as powerful tools capable of generating data that resembles a given distribution.
With their diverse applications across various fields, understanding the training process of generative models becomes paramount.
In this comprehensive guide, we’ll delve into the fundamentals of generative models, explore the training techniques, discuss challenges, and evaluation metrics, and explore real-world applications.
So, let’s embark on this journey of comprehending generative training!
Fundamentals of Generative Models
What are Generative Models?
Generative models are a category of machine learning models that aim to learn the underlying data distribution from a given dataset. Unlike discriminative models which focus on classifying data into specific categories, generative models focus on generating new data points that resemble the original dataset. This unique ability makes them indispensable in various applications, such as image synthesis, text generation, and more.
Types of Generative Models
Generative models come in various flavours, each with its own set of characteristics and use cases:
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