Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection

arXiv:2609.17479 · cs.CV, cs.AI · Submitted 2026-09-15 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-15

Updated: 2026-09-15

DOI: 10.1111/mms.70276

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Explainability — 14× in this paper · explained in 3 episodes
• Model-Agnostic — 14× in this paper · explained in 2 episodes
• Max Saliency Hit Rate — 10× in this paper
• Adaptation — 6× in this paper · explained in 1 episode
• Local Interpretable Model-Agnostic Explanations — 5× in this paper
• Fidelity — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Explainable AI (XAI) — 3× in this paper · explained in 7 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Abundance — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Failure Modes — 2× in this paper · explained in 3 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Verification — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Photorealistic Inpainting for Perturbation-based Explanations in Ecological Monitoring
• Towards A Rigorous Science of Interpretable Machine Learning

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