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We introduce a Gaussian Prototype Layer for gradient-based prototype learning and demonstrate two novel network architectures for explainable segmentation one of which relies on region proposals.
“Semi-supervised classification based on gaussian mixture model for remote imagery,”
Biao Xiong, XiaoJun Zhang, and WanShou Jiang, · 2010
Earlier work this paper cites.
“Slic superpixels,”
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, · 2010
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“Fast r-cnn,”
Ross Girshick, · 2015
Earlier work this paper cites.
“Mask r-cnn,”
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick, · 2017
Earlier work this paper cites.
“Few-shot semantic segmentation with prototype learning.,”
Nanqing Dong and Eric P Xing, · 2018
Earlier work this paper cites.
“This looks like that: deep learning for interpretable image recognition,”
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su, · 2019
Earlier work this paper cites.
“Interpretable image recognition with hierarchical prototypes,”
Peter Hase, Chaofan Chen, Oscar Li, and Cynthia Rudin, · 2019
Earlier work this paper cites.
“Minneapple: A benchmark dataset for apple detection and segmentation,” 2019
Nicolai Häni, Pravakar Roy, and Volkan Isler, · 2019
Cited alongside, same era.
“Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association,”
Thiago T Santos, Leonardo L de Souza, Andreza A dos Santos, and Sandra Avila, · 2020
Cited alongside, same era.
“Minneapple: a benchmark dataset for apple detection and segmentation,”
Nicolai Häni, Pravakar Roy, and Volkan Isler, · 2020
Cited alongside, same era.
“Towards scalable and unified example-based explanation and outlier detection,”
Penny Chong, Ngai-Man Cheung, Yuval Elovici, and Alexander Binder, · 2021
Cited alongside, same era.
“These do not look like those: An interpretable deep learning model for image recognition,”
Gurmail Singh and Kin-Choong Yow, · 2021
Cited alongside, same era.
“But that’s not why: Inference adjustment by interactive prototype deselection,”
Michael Gerstenberger, Sebastian Lapuschkin, Peter Eisert, and Sebastian Bosse, · 2022
Later among the works it cites.
“Interpretable image classification with differentiable prototypes assignment,”
Dawid Rymarczyk, Lukasz Struski, Michał Górszczak, Koryna Lewandowska, Jacek Tabor, and Bartosz Zieliński, · 2022
Later among the works it cites.
“Semi-protopnet deep neural network for the classification of defective power grid distribution structures,”
Stefano Frizzo Stefenon, Gurmail Singh, Kin-Choong Yow, and Alessandro Cimatti, · 2022
Later among the works it cites.
“Protgnn: Towards self-explaining graph neural networks,”
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Cheekong Lee, · 2022
Later among the works it cites.
“Vit-net: Interpretable vision transformers with neural tree decoder,”
Sangwon Kim, Jaeyeal Nam, and Byoung Chul Ko, · 2022
Later among the works it cites.
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Lei Ke, Xia Li, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang, and Fisher Yu, · 2021
Cited alongside, same era.
“Gradient-based training of gaussian mixture models for high-dimensional streaming data,”
Alexander Gepperth and Benedikt Pfülb, · 2021
Cited alongside, same era.
“Deformable protopnet: An interpretable image classifier using deformable prototypes,”
Jon Donnelly, Alina Jade Barnett, and Chaofan Chen, · 2022
Later among the works it cites.
“This looks more like that: Enhancing self-explaining models by prototypical relevance propagation,”
Srishti Gautam, Marina M-C Höhne, Stine Hansen, Robert Jenssen, and Michael Kampffmeyer, · 2023
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