Name
Title | ||
|---|---|---|
A Case-Based Approach for the Selection of Explanation Algorithms in Image Classification |
Abstract | ||
|---|---|---|
Research on eXplainable AI (XAI) is continuously proposing novel approaches for the explanation of image classification models, where we can find both model-dependent and model-independent strategies. However, it is unclear how to choose the best explanation approach for a given image, as these novel XAI approaches are radically different. In this paper, we propose a CBR solution to the problem of choosing the best alternative for the explanation of an image classifier. The case base reflects the human perception of the quality of the explanations generated with different image explanation methods. Then, this experience is reused to select the best explanation approach for a given image. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86957-1_13 | CASE-BASED REASONING RESEARCH AND DEVELOPMENT, ICCBR 2021 |
Keywords | DocType | Volume |
Image explanations, User experience, Case-based explanations | Conference | 12877 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan A. Recio-Garcia | 1 | 121 | 11.03 |
| Humberto Parejas-Llanovarced | 2 | 0 | 0.34 |
| Mauricio Gabriel Orozco-del-Castillo | 3 | 0 | 0.34 |
| Esteban E. Brito-Borges | 4 | 0 | 0.34 |