Semantic Vector

Semantic vectors are numerical representations of the meaning of words, sentences, images, or other data, aiming to capture semantic similarity—objects with similar meanings have similar vectors. Current research focuses on improving the accuracy and efficiency of generating these vectors, often employing techniques like contrastive learning, knowledge distillation, and Siamese networks within various architectures including transformers and autoencoders. These advancements are impacting diverse fields, improving performance in tasks such as brain decoding, natural language processing, and medical image analysis by enabling more nuanced and accurate semantic understanding of complex data.