Showing cs.CVShow all
2 papers · 1 filter
cs.CV2025
CoBELa: Steering Transparent Generation via Concept Bottlenecks on Energy Landscapes
Sangwon Kim, Kyoungoh Lee, Jeyoun Dong +1
Generative concept bottleneck models aim to enable interpretable generation by routing synthesis through explicit, user-facing concepts. In practice, prior approaches often rely on…
cs.CV2024
EQ-CBM: A Probabilistic Concept Bottleneck with Energy-based Models and Quantized Vectors
Sangwon Kim, Dasom Ahn, Byoung Chul Ko +2
The demand for reliable AI systems has intensified the need for interpretable deep neural networks. Concept bottleneck models (CBMs) have gained attention as an effective approach…