GlanceNets: Interpretabile, Leak-proof Concept-based Models

31 May 2022

Papers citing "GlanceNets: Interpretabile, Leak-proof Concept-based Models"

9 / 9 papers shown

Title
If Concept Bottlenecks are the Question, are Foundation Models the Answer? Nicola Debole Pietro Barbiero Francesco Giannini Andrea Passerini Stefano Teso Emanuele Marconato 39 0 0 28 Apr 2025
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts M. Zarlenga Gabriele Dominici Pietro Barbiero Z. Shams M. Jamnik KELM 54 0 0 24 Apr 2025
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models Nishad Singhi Jae Myung Kim Karsten Roth Zeynep Akata 18 1 0 02 May 2024
Understanding Multimodal Deep Neural Networks: A Concept Selection View Chenming Shang Hengyuan Zhang Hao Wen Yujiu Yang 20 5 0 13 Apr 2024
Exploring the Lottery Ticket Hypothesis with Explainability Methods: Insights into Sparse Network Performance Shantanu Ghosh Kayhan Batmanghelich 13 0 0 07 Jul 2023
Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint Borui Zhang Wenzhao Zheng Jie Zhou Jiwen Lu AAML 10 7 0 18 Dec 2022
Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations Wolfgang Stammer Marius Memmel P. Schramowski Kristian Kersting 76 25 0 04 Dec 2021
Conditional Gaussian Distribution Learning for Open Set Recognition Xin Sun Zhen Yang Chi Zhang Guohao Peng K. Ling BDL UQCV 128 214 0 19 Mar 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 156 311 0 07 Feb 2020