Neural networks for generative models
Abstract
Neural networks form the fundamental computational architecture underlying many modern generative models and have significantly expanded the capabilities of artificial intelligence in creating novel and realistic data. This chapter presents the foundational concepts, architectures, and learning mechanisms of neural networks used for generative modelling. It begins by examining the principles of artificial neurons, multilayer networks, activation functions, loss functions, backpropagation, and optimization techniques that enable neural networks to learn complex data representations. The chapter then explores major neural network architectures employed in generative modelling, including autoencoders, variational autoencoders, generative adversarial networks, recurrent neural networks, convolutional neural networks, transformer-based architectures, and diffusion-oriented neural networks. Particular attention is given to representation learning, latent-space modelling, adversarial learning, attention mechanisms, and probabilistic generation. The chapter also discusses the generation of text, images, audio, and multimodal content through neural network-based approaches. Key challenges, including training instability, mode collapse, computational complexity, data dependency, hallucination, interpretability, and evaluation of generated outputs, are examined. Furthermore, the chapter highlights emerging developments in foundation models, multimodal generative systems, and efficient…
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