Content Preservation

Content preservation in various data modalities (text, speech, images) focuses on modifying data while retaining its core informational content. Current research employs diverse approaches, including diffusion models, reinforcement learning with large language models, and generative adversarial networks, often incorporating techniques like adversarial training and content-aware modules to achieve this goal. These advancements are improving applications ranging from speech enhancement and text style transfer to image editing and the creation of 3D models of cultural heritage sites, ultimately enhancing the quality and usability of digital data across numerous fields.