Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

8 July 2023

Papers citing "Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations"

21 / 21 papers shown

Title
RES: A Robust Framework for Guiding Visual Explanation Yuyang Gao Tong Sun Guangji Bai Siyi Gu S. Hong Liang Zhao FAtt AAML XAI 56 32 0 27 Jun 2022
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo Soheil Feizi FAtt 71 83 0 29 Nov 2021
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks Haekyu Park Nilaksh Das Rahul Duggal Austin P. Wright Omar Shaikh Fred Hohman Duen Horng Chau HAI 46 25 0 29 Aug 2021
Learning Debiased Representation via Disentangled Feature Augmentation Jungsoo Lee Eungyeup Kim Juyoung Lee Jihyeon Janel Lee Jaegul Choo CML 46 155 0 03 Jul 2021
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation Seungho Lee Minhyun Lee Jongwuk Lee Hyunjung Shim 66 218 0 19 May 2021
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation Beomyoung Kim Sangeun Han Junmo Kim 61 127 0 12 Mar 2021
Benchmarking and Survey of Explanation Methods for Black Box Models F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 86 225 0 25 Feb 2021
Dissonance Between Human and Machine Understanding Zijian Zhang Jaspreet Singh U. Gadiraju Avishek Anand 98 74 0 18 Jan 2021
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision Max Glockner Ivan Habernal Iryna Gurevych LRM 74 26 0 07 Oct 2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Percy Liang 182 379 0 09 May 2020
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects Zewen Li Wenjie Yang Shouheng Peng Fan Liu HAI 3DV 92 2,694 0 01 Apr 2020
How do Data Science Workers Collaborate? Roles, Workflows, and Tools Amy X. Zhang Michael J. Muller Dakuo Wang FedML AI4CE 60 260 0 18 Jan 2020
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias Krishna Kumar Singh D. Mahajan Kristen Grauman Yong Jae Lee Matt Feiszli Deepti Ghadiyaram 61 109 0 09 Jan 2020
CNN 101: Interactive Visual Learning for Convolutional Neural Networks Zijie J. Wang Robert Turko Omar Shaikh Haekyu Park Nilaksh Das Fred Hohman Minsuk Kahng Duen Horng Chau SSL HAI FAtt 51 26 0 07 Jan 2020
A 20-Year Community Roadmap for Artificial Intelligence Research in the US Y. Gil B. Selman AI4TS 23 54 0 07 Aug 2019
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning Thilo Spinner U. Schlegel H. Schäfer Mennatallah El-Assady HAI 59 237 0 29 Jul 2019
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 79 491 0 09 Jul 2019
The Frontiers of Fairness in Machine Learning Alexandra Chouldechova Aaron Roth FaML 166 415 0 20 Oct 2018
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 62 215 0 17 Jul 2018
Overcoming catastrophic forgetting in neural networks J. Kirkpatrick Razvan Pascanu Neil C. Rabinowitz J. Veness Guillaume Desjardins ... A. Grabska-Barwinska Demis Hassabis Claudia Clopath D. Kumaran R. Hadsell CLL 319 7,478 0 02 Dec 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 256 19,929 0 07 Oct 2016