Over the past decade, the field of generative models in deep learning has witnessed remarkable advancements, revolutionizing various industries.
It is not news that generative models play a crucial role in creating new data samples that resemble the training data, enabling applications like image synthesis, language generation, and data augmentation.
In this article, we will focus on generative methods that harness the power of deep learning, explaining their working principles, applications, and impact on various domains.
Understanding Generative Models
Generative models are algorithms capable of modelling the probability distribution of input data. By contrasting generative models with discriminative models, we can grasp their key differences. Generative models learn how data is generated and can create new samples, while discriminative models focus on distinguishing between different classes.
Deep Learning Fundamentals
Before diving into generative models, let’s briefly review the fundamentals of deep learning. Deep learning, a subset of machine learning, involves the use of artificial neural networks to solve complex problems. These networks consist of multiple layers, each responsible for learning specific features from the data.
Generative Adversarial Networks (GANs)
An Introduction to GANs
Generative Adversarial Networks, or GANs, were introduced by Ian Goodfellow and his colleagues in 2014. GANs employ two neural networks: the generator and the discriminator. The generator generates fake data, while the discriminator tries to distinguish between real and fake data.
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