Conditional generative models are a type of artificial intelligence that creates new data based on specific input conditions or labels. Instead of generating random outputs, these models use extra information to guide what they produce. This allows for more control over the type of data generated, such as producing images of a certain category or…

Variational Autoencoders, or VAEs, are a type of machine learning model that learns to compress data, like images or text, into a simpler form and then reconstructs it back to the original format. They are designed to not only recreate the data but also understand its underlying patterns. VAEs use probability to make their compressed…

Generative Adversarial Networks, or GANs, are a type of artificial intelligence where two neural networks compete to improve each other’s performance. One network creates new data, such as images or sounds, while the other tries to detect if the data is real or fake. This competition helps both networks get better, resulting in highly realistic…

Diffusion models are a type of machine learning technique used to create new data, such as images or sounds, by starting with random noise and gradually transforming it into a meaningful result. They work by simulating a process where data is slowly corrupted with noise and then learning to reverse this process to generate realistic…