基于跳跃网络的转移融合学习
Transferred Fusion Learning using Skipped Networks
摘要：
感兴趣的实体的识别在任何智能系统中都是突出的。在增加识别能力的同时，增强了模型的视觉智能。几种方法，如迁移学习和零样本学习，有助于重用现有的模型或增强现有的模型，以实现改进的性能，在目标识别的任务。转移融合学习就是这样一种机制，它旨在利用两个世界的最佳效果，并构建一个能够超越系统中所涉及的模型的模型。我们提出了一种新的机制，通过引入一个学生架构，网络相互学习，来放大迁移学习的过程。
英文摘要：
Identification of an entity that is of interest is prominent in any intelligent system. The visual intelligence of the model is enhanced when the capability of recognition is added. Several methods such as transfer learning and zero shot learning help to reuse the existing models or augment the existing model to achieve improved performance at the task of object recognition. Transferred fusion learning is one such mechanism that intends to use the best of both worlds and build a model that is capable of outperforming the models involved in the system. We propose a novel mechanism to amplify the process of transfer learning by introducing a student architecture where the networks learn from each other.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
