论文标题
使用跳过网络转移融合学习
Transferred Fusion Learning using Skipped Networks
论文作者
论文摘要
在任何智能系统中，识别感兴趣的实体都是突出的。当添加识别能力时，模型的视觉智能将增强。诸如转移学习和零射击学习之类的几种方法有助于重复现有模型或增强现有模型，以在对象识别任务下提高性能。转移的融合学习是一种这样的机制，它打算使用两全其美的方式，并建立一个能够超过系统模型的模型。我们提出了一种新颖的机制，以通过引入网络相互学习的学生体系结构来扩大转移学习的过程。
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.
