On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917181127655424 |
|---|---|
| author | Song, Junhwa Cha, Keumgang Seo, Junghoon |
| author_facet | Song, Junhwa Cha, Keumgang Seo, Junghoon |
| contents | 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 decision function may yield superior results in ROAR benchmarks, contradicting the original intent of ROAR. This occurrence is similarly observed in the recently introduced variant RemOve-And-Debias (ROAD), and we posit a persistent pattern of blurriness bias in ROAR attribution metrics. Our findings serve as a warning against indiscriminate use on ROAR metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_13836 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective Song, Junhwa Cha, Keumgang Seo, Junghoon Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Methodology 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 decision function may yield superior results in ROAR benchmarks, contradicting the original intent of ROAR. This occurrence is similarly observed in the recently introduced variant RemOve-And-Debias (ROAD), and we posit a persistent pattern of blurriness bias in ROAR attribution metrics. Our findings serve as a warning against indiscriminate use on ROAR metrics. |
| title | On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Methodology |
| url | https://arxiv.org/abs/2304.13836 |