Читать книгу Artificial Intelligence and Quantum Computing for Advanced Wireless Networks - Savo G. Glisic - Страница 59

Classification of images has become a key ingredient in many computer vision applications, for example, image search, robotics, medical imaging, object detection in radar images, or face detection. A particularly popular approach to the problem is based on the use of neural networks.

This lack of interpretability in these solutions is due to the nonlinearity of the various mappings that process the raw image pixels to its feature representation and from that to the final classifier function. This is a considerable drawback in classification applications, as it prevents human experts from carefully verifying the classification decision. A simple yes or no answer is sometimes of limited value in applications where questions like where something occurs or how it is structured are more relevant than a binary or real‐valued one‐dimensional assessment of mere presence or absence of a certain structure. In this section, we aim to explain in more detail the relation between classification and interpretability for multilayered neural networks discussed in the previous chapter.