Outlier Gradient Analysis: Efficiently Improving Deep Learning Model Performance via Hessian-Free Influence Functions

arXiv:2405.03869v1 Announce Type: new
Abstract: Influence functions offer a robust framework for assessing the impact of each training data sample on model predictions, serving as a prominent tool in data-centric learning. Despite their widespread use in various tasks, the strong convexity assumption on the model and the computational cost associated with calculating the inverse of the Hessian matrix pose constraints, particularly when analyzing large deep models. This paper focuses on a classical data-centric scenario--trimming detrimental samples--and addresses both challenges within …

abstract analysis arxiv cs.ai cs.lg data data-centric deep learning framework free functions gradient impact improving influence outlier performance predictions robust sample tasks tool training training data type via