Knowledge Encoding and Interpretation

Abstract

When it comes to interpretability, ML models, particularly DL models, are frequently regarded as a black box due to their complexity and lack of transparency in approach. It is fairly simple to train a network to be specific. A DL model learns to classify an object, recognize text, or generate digital images. It efficiently encapsulates feature learning in the network’s hidden layer, but explainability in brief decreases.