Semantic Encoding

Semantic encoding focuses on representing information's meaning, rather than its raw form, for efficient transmission and processing. Current research emphasizes developing robust and efficient encoding methods across various modalities (speech, video, images, text), often leveraging deep learning architectures like transformers, autoencoders, and diffusion models, alongside techniques such as contrastive learning and knowledge distillation. These advancements are improving data compression, enabling new forms of semantic communication, and facilitating more efficient and robust downstream tasks in areas like autonomous driving, speech synthesis, and recommendation systems.