This Looks Like That: Deep Learning for Interpretable Image Recognition

27 June 2018

Papers citing "This Looks Like That: Deep Learning for Interpretable Image Recognition"

50 / 602 papers shown

Title
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace Jinyung Hong Keun Hee Park Theodore P. Pavlic 29 5 0 25 May 2023
On the Impact of Knowledge Distillation for Model Interpretability Hyeongrok Han Siwon Kim Hyun-Soo Choi Sungroh Yoon 24 4 0 25 May 2023
Fantastic DNN Classifiers and How to Identify them without Data Nathaniel R. Dean D. Sarkar 21 1 0 24 May 2023
Towards credible visual model interpretation with path attribution Naveed Akhtar Muhammad A. A. K. Jalwana FAtt 22 5 0 23 May 2023
What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media Junwei Kuang Jiaheng Xie Zhijun Yan 27 1 0 18 May 2023
FICNN: A Framework for the Interpretation of Deep Convolutional Neural Networks Hamed Behzadi-Khormouji José Oramas 16 0 0 17 May 2023
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization Jayneel Parekh Sanjeel Parekh Pavlo Mozharovskyi Gaël Richard Florence dÁlché-Buc 33 6 0 11 May 2023
Evaluating Post-hoc Interpretability with Intrinsic Interpretability J. P. Amorim P. Abreu João A. M. Santos Henning Muller FAtt 25 1 0 04 May 2023
Discover and Cure: Concept-aware Mitigation of Spurious Correlation Shirley Wu Mert Yuksekgonul Linjun Zhang James Zou 78 56 0 01 May 2023
Interpretability of Machine Learning: Recent Advances and Future Prospects Lei Gao L. Guan AAML 43 31 0 30 Apr 2023
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces Georgii Mikriukov Gesina Schwalbe Christian Hellert Korinna Bade 21 2 0 30 Apr 2023
TABLET: Learning From Instructions For Tabular Data Dylan Slack Sameer Singh LMTD ALM RALM 26 17 0 25 Apr 2023
Learning Bottleneck Concepts in Image Classification Bowen Wang Liangzhi Li Yuta Nakashima Hajime Nagahara SSL 20 42 0 20 Apr 2023
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance Jonathan Crabbé M. Schaar AAML 24 6 0 13 Apr 2023
MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging Yuanyuan Wei Roger Tam Xiaoying Tang MedIm 16 12 0 13 Apr 2023
MLOps Spanning Whole Machine Learning Life Cycle: A Survey Fang Zhengxin Yuan Yi Zhang Jingyu Liu Yue Mu Yuechen ... Xu Xiwei Wang Jeff Wang Chen Zhang Shuai Chen Shiping 16 4 0 13 Apr 2023
Localizing Model Behavior with Path Patching Nicholas W. Goldowsky-Dill Chris MacLeod L. Sato Aryaman Arora 31 85 0 12 Apr 2023
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations Daniel Flores-Araiza F. Lopez-Tiro Jonathan El Beze Jacques Hubert M. González-Mendoza G. Ochoa-Ruiz C. Daul MedIm OOD 32 6 0 08 Apr 2023
Machine Learning with Requirements: a Manifesto Eleonora Giunchiglia F. Imrie M. Schaar Thomas Lukasiewicz AI4TS OffRL VLM 32 5 0 07 Apr 2023
An Interpretable Loan Credit Evaluation Method Based on Rule Representation Learner Zi-yu Chen Xiaomeng Wang Yuanjiang Huang Tao Jia 31 1 0 03 Apr 2023
Why is plausibility surprisingly problematic as an XAI criterion? Weina Jin Xiaoxiao Li Ghassan Hamarneh 52 3 0 30 Mar 2023
Are Data-driven Explanations Robust against Out-of-distribution Data? Tang Li Fengchun Qiao Mengmeng Ma Xiangkai Peng OODD OOD 33 10 0 29 Mar 2023
Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability Soyoun Won Sung-Ho Bae Seong Tae Kim 50 2 0 26 Mar 2023
LINe: Out-of-Distribution Detection by Leveraging Important Neurons Yong Hyun Ahn Gyeong-Moon Park Seong Tae Kim OODD 119 31 0 24 Mar 2023
Take 5: Interpretable Image Classification with a Handful of Features Thomas Norrenbrock Marco Rudolph Bodo Rosenhahn FAtt 40 7 0 23 Mar 2023
Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning Shi Chen Qi Zhao 47 5 0 18 Mar 2023
ICICLE: Interpretable Class Incremental Continual Learning Dawid Rymarczyk Joost van de Weijer Bartosz Zieliñski Bartlomiej Twardowski CLL 32 28 0 14 Mar 2023
Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease Tom Nuno Wolf Sebastian Polsterl Christian Wachinger FAtt 24 6 0 13 Mar 2023
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers S. Sengupta M. Anastasio MedIm 33 6 0 13 Mar 2023
Schema Inference for Interpretable Image Classification Haofei Zhang Mengqi Xue Xiaokang Liu Kaixuan Chen Jie Song Mingli Song OCL 44 3 0 12 Mar 2023
Learning Human-Compatible Representations for Case-Based Decision Support Han Liu Yizhou Tian Chacha Chen Shi Feng Yuxin Chen Chenhao Tan 25 5 0 06 Mar 2023
Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised Domain Adaptation Wenxi Xiao Zhengming Ding Hongfu Liu FAtt 11 2 0 04 Mar 2023
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science P. Bommer M. Kretschmer Anna Hedström Dilyara Bareeva Marina M.-C. Höhne 46 38 0 01 Mar 2023
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals Susu Sun S. Woerner Andreas Maier Lisa M. Koch Christian F. Baumgartner FAtt 35 16 0 01 Mar 2023
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators Yuan Cheng Zu-Yun Shiau Fu-En Yang Yu-Chiang Frank Wang 39 2 0 19 Feb 2023
Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goals Weina Jin Jianyu Fan D. Gromala Philippe Pasquier Ghassan Hamarneh 28 7 0 10 Feb 2023
Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions Mahed Abroshan Saumitra Mishra Mohammad Mahdi Khalili 33 4 0 09 Feb 2023
Red Teaming Deep Neural Networks with Feature Synthesis Tools Stephen Casper Yuxiao Li Jiawei Li Tong Bu Ke Zhang K. Hariharan Dylan Hadfield-Menell AAML 32 15 0 08 Feb 2023
Stop overkilling simple tasks with black-box models and use transparent models instead Matteo Rizzo Matteo Marcuzzo A. Zangari A. Gasparetto A. Albarelli VLM 21 0 0 06 Feb 2023
Neural Insights for Digital Marketing Content Design F. Kong Yuan Li Houssam Nassif Tanner Fiez Ricardo Henao Shreya Chakrabarti 3DV 19 10 0 02 Feb 2023
A Survey of Explainable AI in Deep Visual Modeling: Methods and Metrics Naveed Akhtar XAI VLM 30 7 0 31 Jan 2023
BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete Annotations Yuanhong Chen Yuyuan Liu Chong Wang M. Elliott C. Kwok ... Yu Tian Fengbei Liu Helen Frazer Davis J. McCarthy Gustavo Carneiro 28 3 0 31 Jan 2023
NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classification Parth Padalkar Huaduo Wang Gopal Gupta 21 2 0 30 Jan 2023
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts Mikolaj Sacha Dawid Rymarczyk Lukasz Struski Jacek Tabor Bartosz Zieliñski VLM 32 29 0 28 Jan 2023
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees Johanna Vielhaben Stefan Blücher Nils Strodthoff 27 37 0 27 Jan 2023
Sanity checks and improvements for patch visualisation in prototype-based image classification Romain Xu-Darme Georges Quénot Zakaria Chihani M. Rousset 10 3 0 20 Jan 2023
Learning Support and Trivial Prototypes for Interpretable Image Classification Chong Wang Yuyuan Liu Yuanhong Chen Fengbei Liu Yu Tian Davis J. McCarthy Helen Frazer G. Carneiro 34 24 0 08 Jan 2023
Explainability and Robustness of Deep Visual Classification Models Jindong Gu AAML 39 2 0 03 Jan 2023
Hierarchical Explanations for Video Action Recognition Sadaf Gulshad Teng Long Nanne van Noord FAtt 23 6 0 01 Jan 2023
A Theoretical Framework for AI Models Explainability with Application in Biomedicine Matteo Rizzo Alberto Veneri A. Albarelli Claudio Lucchese Marco Nobile Cristina Conati XAI 25 8 0 29 Dec 2022