## 7 Results and Comparisons  
The training of the models was performed using a Learning Rate of 0.001. Loss function is the cross-entropy error between the predicted and true class. The cross-entropy error was used as the Loss function between the predicted and the true class. For the other parameters of the model, the recommended default settings were set as in [37]. A comparison with the following most widely used supervised Deep Learning models was performed to validate the effectiveness of the proposed architecture. 1. 1-D CNN: The architecture of the 1-D CNN was designed as in [38], and it comprises the input layer, the Convolutional Layer (COL), the max-pooling layer, the fully connected layer and the output one. The number of the Convolutional Filters (COFs) was equal to 20, the length of each filer was 11 and the pooling size had the value of 3. Finally, 100 hidden units were contained in the fully connected layer. 2. 2-D CNN: The 2-D CNN architecture was designed as in [39]. It includes three COLs which were supplied with 4\(\times\)4, 5\(\times\)5 and 4\(\times\)4 CON filters respectively. The COL except of the final one were followed by max-pooling layers. Moreover, the numbers of COFs for the COLs were to 32, 64 and 128, respectively. 3. Simple Convolutional/deconvolutional network with the simple Convolutional blocks and the unpooling function, as it is described in [40,41]. 4. Residual Convolutional/deconvolutional network: Its architecture used residual blocks and a more accurate unpooling function, as it is shown in [42]. The following Tables 5 and 6 show the classification maps which have emerged for the cases of the Pavia University and Pavia Center datasets. Moreover, they present a comparative analysis of the accuracy of the proposed MAME-ZsL algorithm, with the performance of the following classifiers; namely: 1-D CNN, 2-D CNN, Simple Convolutional/Deconvolutional Network (SC/DN), Residual Convolutional/Deconvolutional Network (RC/DN). A single dataset including all records of the Pavia University and Pavia Center datasets was developed, which was employed in an effort to fully investigate the predictive capacity of the proposed system. It was named _General Pavia Dataset (GPD)_, and it was divided into training (Classes 1-7), validation (Classes 8 and 9) and testing subsets (Classes 10 and 11). It is presented in the following Table 7.  
As it can be seen from Table 8, the increase of samples has improved the results in the case of the trained algorithms and in the case of the ZsL technique. It is easy to conclude which the proposed MAME-ZsL is a highly valued Deep Learning system which has achieved remarkable results in all evaluations over their respective competing approaches. The experimental comparison does not include other examples of Zero-shot Learning. This fact does not detract in any case from the value of the proposed method, taking into account that the proposed processing approach builds a predictive model which is comparable to supervised learning systems. The appropriate hyperparameters in the proposed method are also identified by the high reliability and overall accuracy on the obtained results. The final decision was taken based on the performance encountered by the statistical trial and error method. The performance of the proposed model was evaluated against state-of-the-art fully supervised Deep Learning models. It is worth mentioning that the proposed model was trained with instances only from seven classes while the Deep Learning models were trained with instances from all eleven classes. The presented numerical experiments demonstrate that the proposed model produces remarkable results compared to theoretically superior models, providing convincing arguments regarding the classification efficiency of the proposed approach. Another important observation is that it produces accurate results without recurring problems of undetermined cause, because all of the features in the considered dataset are efficiently evaluated.