Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future Directions
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have shown promising results and have outperformed classical machine learning techniques in tasks such as image classification and object recognition. Their human-brain like structure enabled them to learn sophisticated features while passing images through their layers. However, their lack of explainability led to the demand for interpretations to justify their predictions. Research on Explainable AI or XAI has gained momentum to provide knowledge and insights into neural networks. This study summarizes the literature to gain more understanding of explainability in CNNs (i.e., Explainable Convolutional Neural Networks). We classify models that made efforts to improve the CNNs interpretation. We present and discuss taxonomies for XAI models that modify CNN architecture, simplify CNN representations, analyze feature relevance, and visualize interpretations. We review various metrics used to evaluate XAI interpretations. In addition, we discuss the applications and tasks of XAI models. This focused and extensive survey develops a perspective on this area by addressing suggestions for overcoming XAI interpretation challenges, like models’ generalization, unifying evaluation criteria, building robust models, and providing interpretations with semantic descriptions. Our taxonomy can be a reference to motivate future research in interpreting neural networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".