Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

4 April 2019

Papers citing "Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations"

35 / 35 papers shown

Title
ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization Md Rahat-uz- Zaman Bei Wang Paul Rosen 21 0 0 06 May 2025
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations Indu Panigrahi Sunnie S. Y. Kim Amna Liaqat Rohan Jinturkar Olga Russakovsky Ruth C. Fong Parastoo Abtahi FAtt HAI 57 0 0 14 Apr 2025
Understanding the Role of Pathways in a Deep Neural Network Lei Lyu Chen Pang Jihua Wang 27 3 0 28 Feb 2024
Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment Xabier Echeberria-Barrio Amaia Gil-Lerchundi Jon Egana-Zubia Raul Orduna Urrutia AAML 21 6 0 12 Feb 2024
k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis Shashank Kotyan Tatsuya Ueda Danilo Vasconcellos Vargas 27 1 0 07 Dec 2023
Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research Hariharan Subramonyam Jessica Hullman VLM HAI 27 8 0 10 Aug 2023
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations Angelos Chatzimparmpas R. Martins I. Jusufi K. Kucher Fabrice Rossi A. Kerren FAtt 24 160 0 22 Dec 2022
Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks Rusheng Pan Zhiyong Wang Yating Wei Han Gao Gongchang Ou Caleb Chen Cao Jinglin Xu Tong Xu Wei-Neng Chen GNN 18 4 0 21 Dec 2022
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation Reduan Achtibat Maximilian Dreyer Ilona Eisenbraun S. Bosse Thomas Wiegand Wojciech Samek Sebastian Lapuschkin FAtt 27 131 0 07 Jun 2022
ViNNPruner: Visual Interactive Pruning for Deep Learning U. Schlegel Samuel Schiegg Daniel A. Keim VLM 19 2 0 31 May 2022
Sparse Visual Counterfactual Explanations in Image Space Valentyn Boreiko Maximilian Augustin Francesco Croce Philipp Berens Matthias Hein BDL CML 30 26 0 16 May 2022
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps Donald Bertucci M. Hamid Yashwanthi Anand Anita Ruangrotsakun Delyar Tabatabai Melissa Perez Minsuk Kahng 38 29 0 14 May 2022
NOVA: A Practical Method for Creating Notebook-Ready Visual Analytics Zijie J. Wang David Munechika Seongmin Lee Duen Horng Chau GNN 33 10 0 08 May 2022
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI Sami Ede Serop Baghdadlian Leander Weber A. Nguyen Dario Zanca Wojciech Samek Sebastian Lapuschkin CLL 27 6 0 04 May 2022
VisCUIT: Visual Auditor for Bias in CNN Image Classifier Seongmin Lee Zijie J. Wang Judy Hoffman Duen Horng Chau 22 11 0 12 Apr 2022
Visualizing Deep Neural Networks with Topographic Activation Maps A. Krug Raihan Kabir Ratul Christopher Olson Sebastian Stober FAtt AI4CE 28 3 0 07 Apr 2022
ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective Jinbin Huang Aditi Mishra Bum Chul Kwon Chris Bryan FAtt HAI 38 31 0 04 Apr 2022
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 19 1 0 30 Jan 2022
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks Xiwei Xuan Xiaoyu Zhang Oh-Hyun Kwon K. Ma HAI 16 17 0 25 Oct 2021
Explaining Convolutional Neural Networks by Tagging Filters Anna Nguyen Daniel Hagenmayer T. Weller Michael Färber FAtt 19 0 0 20 Sep 2021
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning Xiaoxiao Ma Jia Wu Shan Xue Jian Yang Chuan Zhou Quan Z. Sheng Hui Xiong Leman Akoglu GNN AI4TS 28 538 0 14 Jun 2021
Exploiting Explanations for Model Inversion Attacks Xu Zhao Wencan Zhang Xiao Xiao Brian Y. Lim MIACV 21 82 0 26 Apr 2021
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles G. Cantareira R. Mello F. Paulovich AAML 16 9 0 18 Mar 2021
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry Yingqi Liu Guangyu Shen Guanhong Tao Zhenting Wang Shiqing Ma X. Zhang AAML 22 8 0 16 Mar 2021
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making Md. Naimul Hoque Klaus Mueller CML 51 30 0 03 Jan 2021
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 18 18 0 10 Dec 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 36 7 0 23 Oct 2020
Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models Joseph F DeRose Jiayao Wang M. Berger 15 83 0 03 Sep 2020
A Survey of Visual Analytics Techniques for Machine Learning Jun Yuan Changjian Chen Weikai Yang Mengchen Liu Jiazhi Xia Shixia Liu 19 216 0 21 Aug 2020
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems Mahsan Nourani J. King Eric D. Ragan 12 98 0 20 Aug 2020
DeepStreamCE: A Streaming Approach to Concept Evolution Detection in Deep Neural Networks Lorraine Chambers M. Gaber Z. Abdallah 16 4 0 08 Apr 2020
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence S. Raschka Joshua Patterson Corey J. Nolet AI4CE 18 482 0 12 Feb 2020
Analyzing the Noise Robustness of Deep Neural Networks Kelei Cao Mengchen Liu Hang Su Jing Wu Jun Zhu Shixia Liu AAML 52 89 0 26 Jan 2020
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Siwei Li Li-Wei Chen Michael E. Kounavis Duen Horng Chau FedML AAML 38 224 0 19 Feb 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,235 0 24 Jun 2017