Integrated Gradient

Integrated Gradients (IG) is a method used to explain the predictions of deep neural networks by attributing the model's output to its input features. Current research focuses on improving IG's accuracy and efficiency, addressing issues like noisy visualizations and vulnerability to adversarial attacks, and extending its application to various model architectures, including large language models and those used in image processing and time-series data. These advancements enhance the interpretability of complex models, leading to increased trust and facilitating better understanding of model behavior in diverse scientific and practical applications, such as medical diagnosis and autonomous systems.