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Post-hoc Concept Bottleneck Models

Post-hoc Concept Bottleneck Models

31 May 2022
Mert Yuksekgonul
Maggie Wang
James Y. Zou
ArXivPDFHTML

Papers citing "Post-hoc Concept Bottleneck Models"

36 / 36 papers shown
Title
Concept-Based Unsupervised Domain Adaptation
Concept-Based Unsupervised Domain Adaptation
Xinyue Xu
Y. Hu
Hui Tang
Yi Qin
Lu Mi
Hao Wang
Xiaomeng Li
50
0
0
08 May 2025
If Concept Bottlenecks are the Question, are Foundation Models the Answer?
If Concept Bottlenecks are the Question, are Foundation Models the Answer?
Nicola Debole
Pietro Barbiero
Francesco Giannini
Andrea Passerini
Stefano Teso
Emanuele Marconato
125
0
0
28 Apr 2025
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
Emiliano Penaloza
Tianyue H. Zhan
Laurent Charlin
Mateo Espinosa Zarlenga
40
0
0
25 Apr 2025
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
M. Zarlenga
Gabriele Dominici
Pietro Barbiero
Z. Shams
M. Jamnik
KELM
141
0
0
24 Apr 2025
Interactive Medical Image Analysis with Concept-based Similarity Reasoning
Ta Duc Huy
Sen Kim Tran
Phan Nguyen
Nguyen Hoang Tran
Tran Bao Sam
A. Hengel
Zhibin Liao
Johan W. Verjans
Minh Nguyen Nhat To
Vu Minh Hieu Phan
41
0
0
10 Mar 2025
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models
Itay Benou
Tammy Riklin-Raviv
62
0
0
27 Feb 2025
Concept Layers: Enhancing Interpretability and Intervenability via LLM Conceptualization
Concept Layers: Enhancing Interpretability and Intervenability via LLM Conceptualization
Or Raphael Bidusa
Shaul Markovitch
56
0
0
20 Feb 2025
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens
Samuele Bortolotti
Emanuele Marconato
Paolo Morettin
Andrea Passerini
Stefano Teso
61
2
0
16 Feb 2025
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
Shreyash Arya
Sukrut Rao
Moritz Bohle
Bernt Schiele
68
2
0
28 Jan 2025
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
Divyansh Srivastava
Beatriz Cabrero-Daniel
Christian Berger
VLM
57
8
0
17 Jan 2025
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
Xin-Chao Xu
Yi Qin
Lu Mi
Hao Wang
X. Li
74
9
0
03 Jan 2025
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models
Songning Lai
Jiayu Yang
Yu Huang
Lijie Hu
Tianlang Xue
Zhangyi Hu
Jiaxu Li
Haicheng Liao
Yutao Yue
26
1
0
07 Oct 2024
Image-guided topic modeling for interpretable privacy classification
Image-guided topic modeling for interpretable privacy classification
Alina Elena Baia
Andrea Cavallaro
32
0
0
27 Sep 2024
EQ-CBM: A Probabilistic Concept Bottleneck with Energy-based Models and
  Quantized Vectors
EQ-CBM: A Probabilistic Concept Bottleneck with Energy-based Models and Quantized Vectors
Sangwon Kim
Dasom Ahn
B. Ko
In-su Jang
Kwang-Ju Kim
22
4
0
22 Sep 2024
DEPICT: Diffusion-Enabled Permutation Importance for Image
  Classification Tasks
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks
Sarah Jabbour
Gregory Kondas
Ella Kazerooni
Michael Sjoding
David Fouhey
Jenna Wiens
FAtt
DiffM
32
1
0
19 Jul 2024
Crafting Large Language Models for Enhanced Interpretability
Crafting Large Language Models for Enhanced Interpretability
Chung-En Sun
Tuomas P. Oikarinen
Tsui-Wei Weng
30
6
0
05 Jul 2024
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models
Jayneel Parekh
Quentin Bouniot
Pavlo Mozharovskyi
A. Newson
Florence dÁlché-Buc
SSL
55
1
0
01 Jul 2024
Evidential Concept Embedding Models: Towards Reliable Concept
  Explanations for Skin Disease Diagnosis
Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
Yibo Gao
Zheyao Gao
Xin Gao
Yuanye Liu
Bomin Wang
Xiahai Zhuang
26
1
0
27 Jun 2024
Semi-supervised Concept Bottleneck Models
Semi-supervised Concept Bottleneck Models
Lijie Hu
Tianhao Huang
Huanyi Xie
Chenyang Ren
Zhengyu Hu
Lu Yu
Lu Yu
Ping Ma
Di Wang
49
4
0
27 Jun 2024
A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image
  Analysis
A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis
Yue Yang
Mona Gandhi
Yufei Wang
Yifan Wu
Michael S. Yao
Christopher Callison-Burch
James C. Gee
Mark Yatskar
48
3
0
23 May 2024
Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence
Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence
Thao Le
Tim Miller
Ruihan Zhang
L. Sonenberg
Ronal Singh
29
0
0
13 May 2024
Improving Intervention Efficacy via Concept Realignment in Concept
  Bottleneck Models
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
Nishad Singhi
Jae Myung Kim
Karsten Roth
Zeynep Akata
43
1
0
02 May 2024
Understanding Multimodal Deep Neural Networks: A Concept Selection View
Understanding Multimodal Deep Neural Networks: A Concept Selection View
Chenming Shang
Hengyuan Zhang
Hao Wen
Yujiu Yang
38
5
0
13 Apr 2024
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
Sonia Laguna
Ricards Marcinkevics
Moritz Vandenhirtz
Julia E. Vogt
17
17
0
24 Jan 2024
Codebook Features: Sparse and Discrete Interpretability for Neural
  Networks
Codebook Features: Sparse and Discrete Interpretability for Neural Networks
Alex Tamkin
Mohammad Taufeeque
Noah D. Goodman
25
27
0
26 Oct 2023
Prototype-based Dataset Comparison
Prototype-based Dataset Comparison
N. V. Noord
23
6
0
05 Sep 2023
Exploring the Lottery Ticket Hypothesis with Explainability Methods:
  Insights into Sparse Network Performance
Exploring the Lottery Ticket Hypothesis with Explainability Methods: Insights into Sparse Network Performance
Shantanu Ghosh
Kayhan Batmanghelich
28
0
0
07 Jul 2023
Take 5: Interpretable Image Classification with a Handful of Features
Take 5: Interpretable Image Classification with a Handful of Features
Thomas Norrenbrock
Marco Rudolph
Bodo Rosenhahn
FAtt
27
7
0
23 Mar 2023
Hierarchical Explanations for Video Action Recognition
Hierarchical Explanations for Video Action Recognition
Sadaf Gulshad
Teng Long
N. V. Noord
FAtt
16
6
0
01 Jan 2023
Language in a Bottle: Language Model Guided Concept Bottlenecks for
  Interpretable Image Classification
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification
Yue Yang
Artemis Panagopoulou
Shenghao Zhou
Daniel Jin
Chris Callison-Burch
Mark Yatskar
35
211
0
21 Nov 2022
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
Frederik Pahde
Maximilian Dreyer
Leander Weber
Moritz Weckbecker
Christopher J. Anders
Thomas Wiegand
Wojciech Samek
Sebastian Lapuschkin
55
7
0
07 Feb 2022
Editing a classifier by rewriting its prediction rules
Editing a classifier by rewriting its prediction rules
Shibani Santurkar
Dimitris Tsipras
Mahalaxmi Elango
David Bau
Antonio Torralba
A. Madry
KELM
175
89
0
02 Dec 2021
Fast Model Editing at Scale
Fast Model Editing at Scale
E. Mitchell
Charles Lin
Antoine Bosselut
Chelsea Finn
Christopher D. Manning
KELM
219
341
0
21 Oct 2021
Evaluating Deep Neural Networks Trained on Clinical Images in
  Dermatology with the Fitzpatrick 17k Dataset
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
Matthew Groh
Caleb Harris
L. Soenksen
Felix Lau
Rachel Han
Aerin Kim
A. Koochek
Omar Badri
104
181
0
20 Apr 2021
On Interpretability of Deep Learning based Skin Lesion Classifiers using
  Concept Activation Vectors
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Adriano Lucieri
Muhammad Naseer Bajwa
S. Braun
M. I. Malik
Andreas Dengel
Sheraz Ahmed
MedIm
159
64
0
05 May 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh
Been Kim
Sercan Ö. Arik
Chun-Liang Li
Tomas Pfister
Pradeep Ravikumar
FAtt
122
297
0
17 Oct 2019
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