Fetching the paper…
Reading the bibliography…
The problem of attribution is concerned with identifying the parts of an input that are responsible for a model's output.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Salient deconvolutional networks
Aravindh Mahendran and Andrea Vedaldi · 2016
Earlier work this paper cites.
Visualizing deep convolutional neural networks using natural pre-images
Aravindh Mahendran and Andrea Vedaldi · 2016
Earlier work this paper cites.
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
Earlier work this paper cites.
“Why should I trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
A-fast-rcnn: Hard positive generation via adversary for object detection
Xiaolong Wang, Abhinav Shrivastava, and Abhinav Gupta · 2017
Later among the works it cites.
Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, and Shuicheng Yan · 2017
Later among the works it cites.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Later among the works it cites.
Net2Vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
Later among the works it cites.
Differentiable image parameterizations
Alexander Mordvintsev, Nicola Pezzotti, Ludwig Schubert, and Chris Olah · 2018
Later among the works it cites.
Rise: Randomized input sampling for explanation of black-box models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2017
Cited alongside, same era.
Plug & play generative networks: Conditional iterative generation of images in latent space
Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski · 2017
Cited alongside, same era.
Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
Cited alongside, same era.
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.
Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization
Krishna Kumar Singh and Yong Jae Lee · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Later among the works it cites.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Later among the works it cites.
Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
Later among the works it cites.
Revisiting the importance of individual units in cnns via ablation
Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba · 2018
Later among the works it cites.
github.com/facebookresearch/TorchRay , 2019
Torchray · 2019
Closest in time.
Constrained-cnn losses for weakly supervised segmentation
Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, and Ismail Ben Ayed · 2019
Closest in time.