4 papers · 1 filter
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…
Decompose the model: Mechanistic interpretability in image models with Generalized Integrated Gradients (GIG)
Yearim Kim, Sangyu Han, Sangbum Han +1
In the field of eXplainable AI (XAI) in language models, the progression from local explanations of individual decisions to global explanations with high-level concepts has laid th…
Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition
Sangyu Han, Yearim Kim, Nojun Kwak
The truthfulness of existing explanation methods in authentically elucidating the underlying model's decision-making process has been questioned. Existing methods have deviated fro…
Any-Way Meta Learning
Junhoo Lee, Yearim Kim, Hyunho Lee +1
Although meta-learning seems promising performance in the realm of rapid adaptability, it is constrained by fixed cardinality. When faced with tasks of varying cardinalities that w…