# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 1 Introduction
There has been much research and surveys on transfer learning [34], but most of the work has focused on parameter fine-tuning based on error backpropagation, wherein a network model is developed and trained for one task and then used on a second related--or almost similar--task to maximize the accuracy with small amount of training. Since the Hebbian learning principle addresses the issue of lifetime learning and adaptation through the concept of connection plasticity [35], introducing the principle into the transfer learning algorithm can improve the efficiency in a better way. Referring to the survey of related work [34], transfer learning techniques can be classified mainly as instance re-weighting and feature extraction. Based on work on CNN [36], published work stating the CNNs connection parameter transfer, learning can be implemented either by removing the output layer of a trained network or by implementation of parameter fine-tuning [37]. Training on one-half and later using the second half. The network, which was fine-tuned, surpasses the performance on randomly initialized one. In this paper, we present an algorithm called Hebbian Transfer Learning (HTL), which performs transfer learning on convolutional neural networks with synaptic plasticity in connection weights. It modifies the connection weights and also controls the plasticity coefficients [38; 39; 40] that are responsible for the flexible nature of each connection weight. By using the flexible nature of the network, we have defined the network layers and connection weights of a convolution neural network in multi-part [30], a static and dynamic part. The active part can adjust depending upon the input and the plasticity coefficients. Hence, we can say that the network is performing transfer learning to adapt the connection weights as per the given task. These parameters are both learned and updated during the process of training. To check the effectiveness of the proposed algorithm, experiments are performed with publicly available CIFAR-10 (Canadian Institute for Advanced Research) and CIFAR-100 image datasets. The experimental results show that the proposed transfer learning algorithm with Hebbian learning principle outperforms standard transfer learning approach, especially when the source and target domain are heterogeneous. Main contributions and highlights of the proposed method: the key contribution of this work is to provide a CNN-based hybrid transfer-learning approach using different source pre-trained models to transfer knowledge with the hybrid approach and architecture to accomplish higher accuracy compared to the standard algorithm. The HTL algorithm is the core of the proposed technology. In the proposed method, our aim is to utilize the existing CNN algorithm and do fusion work (e.g., interface work for biology and technology as a research domain). By applying Hebbian learning in transfer learning and deep learning, this method is reusable. It can be used to accelerate the training of neural networks. It is a hybrid CNN architecture. We propose flexible architecture and algorithms, easy to extend algorithms to other deep learning techniques and domains. First time plasticity is applied in transfer learning. We merge multiple solutions to generated optimal solution using algorithm. These applied methods are well proved biologically by [PERSON]. In larger view, we have made conceptual contribution towards transfer learning in deep learning. In addition, the paper also provides the methodological details of the work, which can be utilized by any research group to take the benefit of this work. Therefore, the motivation of the present study was to utilize the power of Hebbian learning rules and machine learning, and enable better accuracy. The idea of proposed Hebbian learning is to let a new algorithm inherit the knowledge of the existing algorithm. Just as the teacher teaches the student knowledge, the higher level of summary knowledge transfer is undoubtedly the fastest and most efficient.