Research map / Label-efficient and robust learning / Recognition and long-tail
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| explanation-by-example (ExMatchina) instead of superimposition methods | DNN explanation · Image (CIFAR-10) | user preference rate: 89.6 vs 50.8 (Grad-CAM++) | Helped |
| explanation-by-example instead of superimposition methods | DNN explanation · Audio | user preference rate: 70.9 vs 48.1 | Helped |
| explanation-by-example instead of superimposition methods | DNN explanation · Sensor (ECG) | user preference rate: 84.8 vs 42.0 | Helped |
| explanation-by-example instead of LIME | DNN explanation · Text (Sentiment140) | user preference rate: 43.7 vs 70.4 | Hurt |
Closest papers by meaning
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- How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations (ICLR 2025 · similarity 0.85)
- Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ (Abstract Reprint) (AAAI 2024 · similarity 0.85)
- F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI (ICLR 2025 · similarity 0.84)
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