Abstract
A successful deep learning model is dependent on extensive training data and processing power and time (known as training costs). There exist many tasks without enough number of labeled data to train a deep learning model. Further, the demand is rising for running deep learning models on edge devices with limited processing capacity and training time. Deep transfer learning (DTL) methods are the answer to tackle such limitations, e.g., fine-tuning a pre-trained model on a massive semi-related dataset proved to be a simple and effective method for many problems. DTLs handle limited target data concerns as well as drastically reduce the training costs. In this paper, the definition and taxonomy of deep transfer learning is reviewed. Then we focus on the sub-category of network-based DTLs since it is the most common types of DTLs that have been applied to various applications in the last decade.
Abstract (translated)
URL
https://arxiv.org/abs/2201.09679
