tgan
TGAN, or Transformer-based Generative Adversarial Network, is a type of generative model that combines the strengths of Transformer architectures with the adversarial training paradigm of Generative Adversarial Networks (GANs). Introduced to address the limitations of traditional GANs, particularly in handling long-range dependencies and sequential data, TGANs leverage the self-attention mechanism of Transformers to generate more coherent and contextually relevant outputs.
The architecture of a TGAN typically consists of two main components: the generator and the discriminator.
TGANs have been successfully applied to various tasks, including text generation, image generation, and time-series forecasting.