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The focus of recent research has shifted from merely improving the metrics based performance of Deep Neural Networks (DNNs) to DNNs which are more interpretable to humans.
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A Typology to Explore the Mitigation of Shortcut Behavior
Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, and Kristian Kersting. 2022 · 2022
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Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning
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Efficient Human-in-the-loop System for Guiding DNNs Attention
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Comparing Human Reasoning and Explainable AI
Carl Johan Helgstrand and Niklas Hultin. 2022 · 2022
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Jan Kronenberger and Anselm Haselhoff. 2020 · 2020
Cited alongside, same era.
Learning interpretable concept-based models with human feedback
Isaac Lage and Finale Doshi-Velez. 2020 · 2020
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Explainable ai: A review of machine learning interpretability methods
Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. 2020 · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. 2020 · 2020
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Generative causal explanations of black-box classifiers
Matthew R. O’Shaughnessy, Gregory H. Canal, Marissa Connor, Mark A. Davenport, and Christopher J. Rozell. 2020 · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting. 2020 · 2020
Cited alongside, same era.
Adversarial TCAV - Robust and Effective Interpretation of Intermediate Layers in Neural Networks
Rahul Soni, Naresh Shah, Chua Tat Seng, and Jimmy D. Moore. 2020 · 2020
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Human-centered concept explanations for neural networks
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Mapping Knowledge Representations to Concepts: A Review and New Perspectives
Lars Holmberg, Paul Davidsson, and Per Linde. 2022 · 2022
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Proto2Proto: Can you recognize the car, the way I do?. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10233–10243
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Towards learning to explain with concept bottleneck models: mitigating information leakage
Joshua Lockhart, Nicolas Marchesotti, Daniele Magazzeni, and Manuela Veloso. 2022 · 2022
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GlanceNets: Interpretabile, Leak-proof Concept-based Models
Emanuele Marconato, Andrea Passerini, and Stefano Teso. 2022 · 2022
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Acquisition of chess knowledge in alphazero
Thomas McGrath, Andrei Kapishnikov, Nenad Tomašev, Adam Pearce, Martin Wattenberg, Demis Hassabis, Been Kim, Ulrich Paquet, and Vladimir Kramnik. 2022 · 2022
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Gayda Mutahar and Tim Miller. 2022 · 2022
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Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models
Ainkaran Santhirasekaram, Avinash Kori, Andrea Rockall, Mathias Winkler, Francesca Toni, and Ben Glocker. 2022 · 2022
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A framework for learning ante-hoc explainable models via concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10286–10295
Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar, and Vineeth N Balasubramanian. 2022 · 2022
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C-SENN: Contrastive Self-Explaining Neural Network
Yoshihide Sawada and Keigo Nakamura. 2022a · 2022
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Concept Bottleneck Model With Additional Unsupervised Concepts
Yoshihide Sawada and Keigo Nakamura. 2022b · 2022
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Concept Embedding Analysis: A Review
Gesina Schwalbe. 2022 · 2022
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Right for the right latent factors: Debiasing generative models via disentanglement
Xiaoting Shao, Karl Stelzner, and Kristian Kersting. 2022 · 2022
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Understanding and Enhancing Robustness of Concept-based Models
Sanchit Sinha, Mengdi Huai, Jianhui Sun, and Aidong Zhang. 2022 · 2022
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Interactive disentanglement: Learning concepts by interacting with their prototype representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10317–10328
Wolfgang Stammer, Marius Memmel, Patrick Schramowski, and Kristian Kersting. 2022 · 2022
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Leveraging Explanations in Interactive Machine Learning: An Overview
Stefano Teso, Öznur Alkan, Wolfgang Stammer, and Elizabeth M. Daly. 2022 · 2022
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HINT: Hierarchical Neuron Concept Explainer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10254–10264
Andong Wang, Wei-Ning Lee, and Xiaojuan Qi. 2022 · 2022
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Beyond explaining: Opportunities and challenges of XAI-based model improvement
Leander Weber, Sebastian Lapuschkin, Alexander Binder, and Wojciech Samek. 2022 · 2022
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Mengqi Xue, Qihan Huang, Haofei Zhang, Lechao Cheng, Jie Song, Minghui Wu, and Mingli Song. 2022 · 2022
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Post-hoc concept bottleneck models
Mert Yuksekgonul, Maggie Wang, and James Zou. 2022 · 2022
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A Survey of Explainable AI in Deep Visual Modeling: Methods and Metrics
Naveed Akhtar. 2023 · 2023
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Interpretable neural-symbolic concept reasoning. In International Conference on Machine Learning . PMLR, 1801–1825
Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lió, Frederic Precioso, Mateja Jamnik, and Giuseppe Marra. 2023 · 2023
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Concept-level debugging of part-prototype networks
Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, and Andrea Passerini. 2023 · 2023
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Human uncertainty in concept-based ai systems. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society . 869–889
Katherine Maeve Collins, Matthew Barker, Mateo Espinosa Zarlenga, Naveen Raman, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, and Krishnamurthy Dvijotham. 2023 · 2023
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State2explanation: Concept-based explanations to benefit agent learning and user understanding
Devleena Das, Sonia Chernova, and Been Kim. 2023 · 2023
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A Protocol for Evaluating Model Interpretation Methods from Visual Explanations
Hamed Behzadi Khormuji and José Oramas. 2023 · 2023
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Neuro Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, and Stefano Teso. 2023a · 2023
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Neuro Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, and Stefano Teso. 2023b · 2023
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ProtoSeg: Interpretable Semantic Segmentation With Prototypical Parts. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 1481–1492
Mikołaj Sacha, Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, and Bartosz Zieliński. 2023 · 2023
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Learning Support and Trivial Prototypes for Interpretable Image Classification
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Towards Robust Metrics for Concept Representation Evaluation
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Learning to Receive Help: Intervention-Aware Concept Embedding Models
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