Temporal Gan
Temporal GANs (TGANs) are generative adversarial networks designed to model and generate data sequences exhibiting temporal dependencies, addressing challenges in various fields where time-series data is crucial. Current research focuses on adapting TGAN architectures, such as incorporating LSTMs or Siamese networks, to specific applications including medical image segmentation, domain translation (e.g., optical to SAR image conversion), and human mobility prediction. These models offer significant potential for augmenting limited datasets, improving model robustness, and enabling new analyses in diverse domains ranging from healthcare to remote sensing. The ability to generate realistic and temporally consistent synthetic data is a key advantage driving ongoing research.