Główne pojęcia
Pantypes empower self-explainable models by capturing diverse regions in the latent space, enhancing interpretability and fairness.
Streszczenie
Abstract:
Prototypical classifiers aim for transparency.
Representation bias affects machine learning diversity.
Pantypes introduced to enhance diversity and fairness.
Introduction:
ML systems impact society, leading to explainable AI.
Two approaches: black-box explanations vs. self-explainable models (SEMs).
SEMs require transparency, trustworthiness, and diversity.
Data Extraction:
"arXiv:2403.09383v1 [stat.ML] 14 Mar 2024"
Results:
PanVAE shows higher predictive performance than ProtoPNET.
PanVAE achieves better prototype representation quality and data coverage.
Conclusion:
Pantypes improve model interpretability without compromising accuracy.
Statystyki
"arXiv:2403.09383v1 [stat.ML] 14 Mar 2024"