Fetching the paper…
Reading the bibliography…
Understanding intermediate layers of a deep learning model and discovering the driving features of stimuli have attracted much interest, recently.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Interactive graph cuts for optimal boundary & region segmentation of objects in nd images
Yuri Y Boykov and M-P Jolly · 2001
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Deconvolutional networks
Matthew D Zeiler, Dilip Krishnan, Graham W Taylor, and Rob Fergus · 2010
Earlier work this paper cites.
Adaptive deconvolutional networks for mid and high level feature learning
Matthew D Zeiler, Graham W Taylor, and Rob Fergus · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
The taylor decomposition: A unified generalization of the oaxaca method to nonlinear models
Stephen Bazen and Xavier Joutard · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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.
Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
Cited alongside, same era.
Is object localization for free?-weakly-supervised learning with convolutional neural networks
Maxime Oquab, Léon Bottou, Ivan Laptev, and Josef Sivic · 2015
Cited alongside, same era.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Cited alongside, same era.
Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
Cited alongside, same era.
Deepred–rule extraction from deep neural networks
Jan Ruben Zilke, Eneldo Loza Mencía, and Frederik Janssen · 2016
Cited alongside, same era.
Interpretable deep models for icu outcome prediction
Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu · 2016
Cited alongside, same era.
Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
Later among the works it cites.
Learning how to explain neural networks: Patternnet and patternattribution
Pieter-Jan Kindermans, Kristof T Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, and Sven Dähne · 2017
Later among the works it 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
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Later among the works it cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Treeview: Peeking into deep neural networks via feature-space partitioning
Jayaraman J Thiagarajan, Bhavya Kailkhura, Prasanna Sattigeri, and Karthikeyan Natesan Ramamurthy · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Self-taught object localization with deep networks
Loris Bazzani, Alessandra Bergamo, Dragomir Anguelov, and Lorenzo Torresani · 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.
Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
Cited alongside, same era.
Deep convolutional neural networks for image classification: A comprehensive review
Waseem Rawat and Zenghui Wang · 2017
Cited alongside, same era.
Zhuwei Qin, Fuxun Yu, Chenchen Liu, and Xiang Chen · 2018
Later among the works it cites.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
Later among the works it cites.
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
Later among the works it cites.
Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Nicolas Papernot and Patrick McDaniel · 2018
Later among the works it cites.
Interpretable convolutional neural networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
Later among the works it cites.
innvestigate neural networks
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, and Pieter-Jan Kindermans · 2019
Later among the works it cites.
Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al · 2020
Closest in time.