Non Parallel

Non-parallel methods address the challenge of transforming data (e.g., speech, molecules) without requiring paired examples of the source and target states. Current research focuses on developing generative models, such as diffusion models, normalizing flows, and generative adversarial networks (GANs), often incorporating techniques like style transfer and disentanglement to achieve high-fidelity conversions while preserving desired attributes (e.g., linguistic content in speech). These advancements are significant for applications like voice conversion, speech emotion modification, and molecular optimization, enabling efficient data augmentation and the creation of new data with specific properties. The ability to learn from unpaired data significantly expands the scope of machine learning applications in various fields.