Meta Embedding

Meta-embedding integrates multiple existing embeddings (e.g., word, image, audio) to create a more comprehensive and robust representation. Research focuses on optimizing the fusion of these source embeddings, exploring techniques like weighted concatenation and meta-learning algorithms (such as MAML) to improve accuracy and address issues like bias amplification. This approach enhances performance in various applications, including recommender systems, medical image diagnosis, and audio retrieval, by leveraging diverse information sources for improved model accuracy and personalization.