Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI.

ABSTRACT: AI driven by deep learning is transforming many aspects of science and technology. The enormous success of deep learning stems from its unique capability of extracting essential features from Big Data for decision-making. However, the feature extraction and hidden representations in deep neural networks (DNNs) remain inexplicable, primarily because of lack of technical tools to comprehend and interrogate the feature space data. The main hurdle here is that the feature data are often noisy in nature, complex in structure, and huge in size and dimensionality, making it intractable for existing techniques to analyze the data reliably. In this work, we develop a computational framework named contrastive feature analysis (CFA) to facilitate the exploration of the DNN feature space and improve t

SUBMITTER: Islam MT 

PROVIDER: S-EPMC11296119 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature