Explaining Deep Neural Network using Layer-wise Relevance Propagation and Integrated Gradients
Overview of explainability for deep neural networks using LRP and Integrated Gradients.
- Published in: IEEE
- DOI: 10.1109/SAMI50585.2021.9378686
- ISBN: 978-1-7281-8054-0
- Link: https://ieeexplore.ieee.org/abstract/document/9378686
The field of artificial intelligence is the subject of research by a wide scientific community. In particular, through improved methodology, the availability of big data, and increased computing power, today’s machine learning algorithms can achieve excellent performance that sometimes even exceeds the human level. However, due to their nested nonlinear structure, these models are generally considered to be “Black boxes” that do not provide any information about what exactly leads them to provide a specific output. This raised the need to interpret these algorithms and understand how they work as they are applied even in areas where they can cause critical damage.