About this document
Understanding Generative Adversarial Networks by ishaswami2003 is a document available to read on EtoBox.
Generative Adversarial Networks (GANs) are a class of machine learning frameworks used for generating synthetic data, with various architectures like DCGAN, cGAN, and StyleGAN. Recent trends indicate a shift towards diffusion models for image generation, while GANs remain relevant in specific applications such as synthetic data generation and video creation. Ethical considerations include concerns over deepfakes, privacy, and bias amplification, necessitating careful evaluation and regulation.
- Author
- ishaswami2003
- Language
- EN