Fetching the paper…
Reading the bibliography…
In this paper, we propose an efficient saliency map generation method, called Group score-weighted Class Activation Mapping (Group-CAM), which adopts the "split-transform-merge" strategy to generate saliency maps.
Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Earlier work this paper cites.
Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
Earlier work this paper cites.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
XRAI: better attributions through regions
Andrei Kapishnikov, Tolga Bolukbasi, Fernanda B. Viégas, and Michael Terry · 2019
Later among the works it cites.
SAM: the sensitivity of attribution methods to hyperparameters
Naman Bansal, Chirag Agarwal, and Anh Nguyen · 2020
Later among the works it cites.
Visualizing deep networks by optimizing with integrated gradients
Zhongang Qi, Saeed Khorram, and Fuxin Li · 2020
Later among the works it cites.
There and back again: Revisiting backpropagation saliency methods
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, and Andrea Vedaldi · 2020
Later among the works it cites.
Score-cam: Score-weighted visual explanations for convolutional neural networks
Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, and Xia Hu · 2020
Later among the works it cites.
Attribution in scale and space
Shawn Xu, Subhashini Venugopalan, and Mukund Sundararajan · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader, and Vineeth N. Balasubramanian · 2018
Cited alongside, same era.
RISE: randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Cited alongside, same era.
Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
Cited alongside, same era.
Later among the works it cites.
A novel visual interpretability for deep neural networks by optimizing activation maps with perturbation
Qing-Long Zhang, Lu Rao, and Yubin Yang · 2021
Closest in time.