all AI news
On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective. (arXiv:2304.13836v3 [cs.LG] UPDATED)
cs.CV updates on arXiv.org arxiv.org
Approaches for appraising feature importance approximations, alternatively
referred to as attribution methods, have been established across an extensive
array of contexts. The development of resilient techniques for performance
benchmarking constitutes a critical concern in the sphere of explainable deep
learning. This study scrutinizes the dependability of the RemOve-And-Retrain
(ROAR) procedure, which is prevalently employed for gauging the performance of
feature importance estimates. The insights gleaned from our theoretical
foundation and empirical investigations reveal that attributions containing
lesser information about the …
arxiv attribution benchmarking data data processing deep learning development feature importance inequality performance perspective processing resilient sphere study