# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation
## 2 Background and Index Terms
### Transfer Learning vs. Machine Learning  
A typical hypothesis in classical machine learning is that the training and testing data have the same feature space and data distributions, as shown in Figure 2a. When a new job arrives with a different data distribution than the prior one, a new model should be built from the ground up using the current data. Such solutions necessitate tremendous effort and thus are, in most situations, extremely expensive.  
The concept of transfer learning was developed to speed up the learning process and acquire better alternatives [19]. It was influenced by the fact that human beings may intelligently use the knowledge obtained in the past when dealing with a problem they have never encountered previously [20; 21]. In contrast to typical machine learning approaches, transfer learning sustains data distribution differences. It applies the knowledge collected from other sources to the target task, as shown in Figure 2b.  
Figure 1: Bifurcation diagram of Gauss map.  
Figure 2: Typical (**a**) Machine Learning and (**b**) Transfer Learning processes.  
Deep learning implementations frequently employ transfer learning. A pre-trained network can be used as a preliminary step for learning a new task. Transfer learning is often considerably quicker and more efficient than training a network from the start with randomly initialized weights for fine-tuning [20]. With a reduced number of training images, it is possible to feed learned characteristics to a fresh, swiftly The following are the key benefits of transfer learning over machine learning:  
* It allows you to train systems with less labeled data by reusing popular models that have previously been trained on massive datasets, making transfer learning a popular approach;
* It has the potential to cut down on training time and computer resources. The weights are not learned from starting with transfer learning since the pre-trained model has previously learned them based on earlier learning;
* You can use model topologies produced by the deep learning scientific community, such as AlexNet, GoogLeNet, and ResNet, which are popular designs The Transfer Learning Workflow of Figure 3 is as follows:  
1. Selecting a pre-trained model: It is simpler to function with pre-trained models since there are many of them accessible on multiple platforms such as Googlenet, Squeezenet, and others;
2. Replacement of final layers: The final layers of the chosen pre-trained model are changed to retrain the network and to categorize a fresh batch of images and classes. The final wholly linked layer is changed to get a similar number of nodes as the number of new classes and a new classification layer that will provide an output based on the probabilities estimated by the layer. The final wholly linked layer will describe the new number of network classes that will it learn after the layers have been modified. The classification layer will select outputs from the new output categories accessible after modifying the layers;
3. Freezing the weights (Optional): By limiting the learning rates in such layers to zero, the weights of earlier layers in the network may be frozen. As a result, the characteristics of frozen layers are not modified throughout training; this significantly accelerates network training. In addition, freezing weights can help the network prevent overfitting if the new data set is tiny;
4. Retraining the model. The network will be retrained to understand and recognize the attributes associated with the new data and categories. Retraining usually needs less data than training a model from the start;
5. Predicting and assessing network accuracy. For example, one may classify fresh images and evaluate how well the network works once the model has been retrained [22].  
Figure 3: Classification with pre-trained deep network.