# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 1 Introduction
Knowledge transfer and transfer learning can reduce the effort of training deep learning models from scratch. Let us see the problem from the biological perspective. It is a fact that evolutionary learning in the biological brain, over millions of years, is a contribution of the brain's ability to change existing learning concerning experience gained. The genetic material is responsible for carrying evolutionary information from generation to generations. This explains that a large number of neural connections and their plasticity is learned rather than genetically coded. We see a need where the AI systems and techniques are more influenced by actual brain function, are more flexible, and not just the mathematical formula approach. However, most of the standard transfer learning algorithms are designed to repeat the same method for fine-tuning of the weights on the target domain. However, the human brains mechanism of learning a new complex concept is different from just the repetition of the same method of learning on a different domain. The Hebbian theory, introduced by [PERSON], explains the \"discriminative associative learning\". This particular behavior of Hebbian learning makes it a very viable candidate for discriminative learning for the search of the specific feature for the task of object recognition or image classification. The Hebbian rule is both local and incremental. Referring research literature to support the theory that when two neurons fire together, causing the sequenced activations in individual brain cells, commonly called pre- and post-synaptic spikes. Study on the visual cortical circuit, and its relationship to particular learning to induce plasticity, have proved to be of great significance; agents that can learn from experience can be treated as the problem of learning of the learning. The techniques used in the article give attention to the problem of learning in its entirety. Method is learning how to modify the parameters of the target model and target hyper-parameters as well. However, the concept of plastic-learning for transfer learning, that is, the learning to learn the transfer learning using the synaptic plastic networks in neural networks, is a novel attempt. The significance of using synaptic plasticity in neural networks as a source of meta-learning has enormous potential. Plasticity at the very local level of a neuron-to-neuron connection, when used as an enhancement to neural network, may learn any independent memory behavior. Learning to discriminate between instances of different classes, over a variable number of classes within the dataset space defined by the task at hand, can be the result-oriented approach for classification problem. HTL technique is a framework that offers comprehensive yet individualized solutions for all the different applied domains. The object in the transfer process has two parts: network structure and weights. Technique transfers both structure and weights of network simultaneously. Our experiment uses significantly smaller source dataset and (relatively) not so large target task dataset. Other transfer learning techniques use larger datasets as source task datasets [16]. Our study is similar to this approach, to evaluate the effectiveness of transfer learning methods in the repurposed heterogeneous domain [17]. The need for such method is quite significant, for example: many scenarios where the data set is smaller, or unlabeled data are available and labeled data are much less. In such cases, the technique of transfer learning that is to learn from the available dataset, and using the learned knowledge on a new domain dataset, is very useful. In many cases, the source data and target dataset have different feature space of image data, or different data label space as well. In this situation, the heterogeneous transfer learning plays a significant part, and learning the knowledge from one domain and transferring the knowledge to totally different data domain becomes possible.