# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 1 Introduction  
The biological structure and behavior of real animal brain neurons has inspired the neural networks [1], and backpropagation [2] has evolved to be one of the most effective standard learning rules for artificial neural networks. The supervised learning of neural networks utilize training datasets and a global loss function. The gradient provided by the loss function [3] is back propagated from the output layer to hidden layers to update the parameters of the network. Many advanced optimizing techniques have been developed for gradient descent [4], and various neural network models have been proposed and successfully applied for image classification tasks, including the Convolutional Neural Networks (CNN), such as AlexNet [5] and VGGNet [6]. Today's deep learning models can reach human-level accuracy in analyzing and segmenting an image [7]. These kinds of methods are considered tedious and time-consuming, and require experts in the field, especially in the feature extraction and selection tasks [8]. Recent studies have shown that machine learning methods can produce promising results on tasks, such as image classification, detection, and segmentation in different fields of computer vision and image processing. Training these deep learning algorithms from scratch to produce accurate results, and avoid overfitting, remain an issue due to the lack of labeled images for experiments [9]. Apart from these significant achievements, CNNs work very well on large datasets. However, most of the time they fail on small datasets if proper care is not taken. To meet the same level of performance, even on a small dataset, and to classify, we need approaches like transfer learning using pre-trained models trained on source-target architectural techniques. In recent years, some techniques, such as transfer learning and image augmentation, have shown promising opportunities towards increasing the number of training data, overcoming overfitting, and producing robust results [10]. Another such approach is active transfer learning [11]. However, these pre-trained networks when trained with domain, which do not contain lots of labeled images related to target domain, lead to poor performance. To solve this challenge, a transfer learning technique, whose major research focus is how to store the knowledge gained when solving a problem, and apply it to different but related problems, would be needed and desirable to reduce the amount of development and data collection costs and improve the performance of algorithms in the target domain. There are already many examples in knowledge engineering where transfer learning can be truly promising and useful, including object recognition, image classification, and natural language processing [12; 13; 14]. One of the solutions is proposed by methods such as the Hebbian transfer learning (HTL) algorithm. It will be a valuable contribution, to simplify the difference between the source and target domain problem in transfer learning. The proposed technique can be seen as taking pre-trained systems as backbone and adding high-level functions to the existing architecture. What and how to transfer are vital issues to be addressed, as different methods apply to the different source-target domain. The proposed approach has demonstrated a significant improvement in classification accuracy and performance, making it more suitable for heterogeneous study with less labeled domains, such as medical imaging. The proposed technique will overcome the lack of available training samples issues, improve the pre-trained models accuracy and performance, and will provide a valuable solution to the difference between the source and target domain problem in transfer learning. Transfer learning in the convolutional neural network is the answer to the problem of the need for large data and computing power. The most appropriate TL (\"Transfer Learning\") technique, in a situation with deficient datasets, was used due to deficient datasets in research [15].