3 papers
cs.CV2024
Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings
Jinyung Hong, Yearim Kim, Keun Hee Park +3
Inner interpretability is a promising field aiming to uncover the internal mechanisms of AI systems through scalable, automated methods. While significant research has been conduct…
cs.CV2024
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
Jinyung Hong, Eun Som Jeon, Changhoon Kim +5
Biased attributes, spuriously correlated with target labels in a dataset, can problematically lead to neural networks that learn improper shortcuts for classifications and limit th…
cs.LG2023
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace
Jinyung Hong, Keun Hee Park, Theodore P. Pavlic
Many interpretable AI approaches have been proposed to provide plausible explanations for a model's decision-making. However, configuring an explainable model that effectively comm…