IDENTIFYING TRANSFER MODELS FOR MACHINE LEARNING TASKS

The invention relates to identifying transfer models for machine learning tasks. Techniques regarding autonomously facilitating the selection of one or more transfer models to enhance the performanceof one or more machine learning tasks are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can assess a similarity metric between a source data set and a sample data set from a target machine learning task. The computer executable components can also comprise an identification component that can identify a pre-trained neural network model associated with the source data set based on the similarity metric to perform the target machine learning task. 本申请涉及识别用于机器学习任务的迁移模型。提供了关于自主地促