## 5 Application of the MAME-ZsL in Hyperspectral Image Analysis
Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. The experiments show that the proposed fine-grained object recognition model achieves only 14.3% recognition accuracy for the classes with no training examples. This is slightly better than a random guess accuracy of 6.3%. Another method [27] automatically creates a training dataset from a single degraded image and trains a denoising network without any clear images. However, the performance of the proposed method shows the same performance as the optimization-based method at high noise levels. [PERSON] et al., 2015, proposed a time-consuming and resource depended model [28] which learns to perform zero-shot classification, using a meta-learner which is trained to produce corrections to the output of a previously trained learner. The model consists of a Task Module (TM) which supplies an initial prediction, and a Correction Module (CM) updating the initial prediction. The TM is the learner and the CM is the meta-learner. The correction module is trained in an episodic approach, whereby many different task modules are trained on various subsets of the total training data, with the rest being used as unseen data for the CM. The correction module takes as input a representation of the TM's training data to perform the predicted correction. The correction module is trained to update the task module's prediction to be closer to the target value. In addition [29] proposes the use of the visual space as the embedding one. In this space, the subsequent nearest neighbor search suffers much less from the harness problem and it becomes more effective. This model design also provides a natural mechanism for multiple semantic modalities (e.g., attributes and sentence descriptions) to be fused and optimized jointly in an end-to-end manner. Only the statistics of the current environment are used and the trained process must include a variety of different statistics from different tasks and environments. Additionally, [30] propose a very promising approach with high-grade accuracy, but the model is characterized by high bias. In the case of image classification, various spatial information can be extracted and used, such as edges, shapes and associated color areas. As they are organized into multiple levels, they are hierarchically separated into levels of abstraction, creating the conditions for selecting the most appropriate features for the training process. Utilizing the above processes, ZsL inspires and simulates the functions of human visual perception, where multiple functional levels and intermediate representations are developed, from capturing an image to the retina to responding in stimuli. This function is based on the conversion of the input representation to a higher level one, as it is performed by each intermediate unit. High-level features are more general and unchanged,while low-level ones help to categorize inputs. Their effectiveness is interpreted on the basis of the \"_universal approximation theorem_,\" which deals with the ability of a neural structure to approach continuous functions and the probabilistic inference which considers the activation of nonlinearity as a function of cumulative distribution. This is related to the concepts of optimization and generalization respectively [25]. Given that in deep neural networks, each hidden level trains a distinct set of features, coming from the output of the previous level, further operation of this network enables the analysis of the most complex features, as they are reconstructed and decomposed from layer to layer. This hierarchy, as well as the degradation of information, while increasing the complexity of the system, also enables the handling of high-dimensional data, which pass through non-linear functions. It is thus possible to discover unstructured data and to reveal a latent structure in unmarked data.