👉 Twins computing, also known as twin algorithms or twin models, refers to a computational technique where two separate but related models are trained simultaneously on the same dataset or closely aligned datasets, often with a shared parameter space or initialization. This approach leverages the complementary strengths of each model to improve overall performance, particularly in tasks like generative modeling, where one twin might focus on capturing detailed patterns while the other handles broader structural aspects. By learning from each other, twins can achieve more robust and accurate results than single models, often requiring less data and computational resources to outperform their standalone counterparts.
