Visualization and Interpretation of Latent Space in Deep Learning

Deep learning-based image classification represents a significant advancement in enabling computers to identify the content of images. This process involves a component known as the feature extractor, which extracts vital features from images, followed by a classifier that uses this information to determine the category to which an image belongs. For enhancing classification accuracy, it is crucial to efficiently extract latent features, which are the hidden information within images. Despite the high accuracy of some models, the detailed mechanisms through which these latent features are effectively extracted remain insufficiently understood. In this study, we delve into how the feature extractor contributes to information retrieval from images. Specifically, we analyze the nature of feature vectors generated when employing categorical cross-entropy and how these vectors aid the classifier’s decision-making process. By representing these feature vectors in two dimensions, we can visually depict them, deepening our understanding of the interplay between feature extractors and classifiers. Through this approach, our goal is to elucidate the underlying mechanisms of high-precision image classification models, bringing us closer to unraveling the complexities behind efficient feature extraction and classification.