SYSTEMS AND METHODS FOR ACTIVE TRANSFER LEARNING WITH DEEP FEATURIZATION

Systems and methods for active transfer learning in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a deep featurizer, wherein the method comprises training a master model and a set of one or more secondary models, wherein the master model includes a set of one or more layers, freezing weights of the master model, generating a set of one or more outputs from the master model, and training a set of one or more orthogonal models on the generated set of outputs. 例示了根据本发明的实施例的用于主动迁移学习的系统和方法。一个实施例包括一种用于训练深度特征化器的方法，其中该方法包括：训练主模型和一组一个或多个辅助模型，其中主模型包括一个或多个层的集合；冻结主模型的权重；从主模型生成一组一个或多个输出；并且在生成的一组输出上训练所述一组一个或多个正交模型。