Auto Tagging

Auto-tagging uses machine learning to automatically assign descriptive tags to various data types, such as music tracks, scientific papers, or images, streamlining organization and retrieval. Current research emphasizes improving accuracy and efficiency, particularly for less-common tags ("long tail") and using limited training data, employing techniques like few-shot learning, self-supervised learning, and contrastive learning with architectures such as InceptionV3 and variations of recurrent neural networks. These advancements are crucial for managing the ever-increasing volume of digital data across diverse fields, improving data accessibility and facilitating more effective knowledge discovery and information retrieval.