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Feature visualization has gained substantial popularity, particularly after the influential work by Olah et al.
The importance of phase in signals
Alan V Oppenheim and Jae S Lim · 1981
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Visual sensitivity to two-dimensional spatial phase
Terry Caelli and Paul Bevan · 1982
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Image phase or amplitude? rapid scene categorization is an amplitude-based process
Nathalie Guyader, Alan Chauvin, Carole Peyrin, Jeanny Hérault, and Christian Marendaz · 2004
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Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise
Olivier R Joubert, Guillaume A Rousselet, Michele Fabre-Thorpe, and Denis Fize · 2009
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Visualizing googlenet classes
M. Øygard Audun · 2015
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Inceptionism: Going deeper into neural networks
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015
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Understanding intra-class knowledge inside cnn
Donglai Wei, Bolei Zhou, Antonio Torrabla, and William Freeman · 2015
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On the role of spatial phase and phase correlation in vision, illusion, and cognition
Evgeny Gladilin and Roland Eils · 2015
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Class visualization with bilateral filters. 2016
Mike Tyka · 2016
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Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2016
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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
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Incorporating nesterov momentum into adam
Timothy Dozat · 2016
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 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.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C. Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Plug & play generative networks: Conditional iterative generation of images in latent space
Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis
Thomas Fel, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, and Thomas Serre · 2021
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Sharpening local interpretable model-agnostic explanations for histopathology: improved understandability and reliability
Mara Graziani, Iam Palatnik de Sousa, Marley MBR Vellasco, Eduardo Costa da Silva, Henning Müller, and Vincent Andrearczyk · 2021
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What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods
Julien Colin, Thomas Fel, Rémi Cadène, and Thomas Serre · 2021
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The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Giang Nguyen, Daeyoung Kim, and Anh Nguyen · 2021
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Reliable post hoc explanations: Modeling uncertainty in explainability
Dylan Slack, Anna Hilgard, Sameer Singh, and Himabindu Lakkaraju · 2021
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Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
Cited alongside, same era.
Later among the works it cites.
Plug-in inversion: Model-agnostic inversion for vision with data augmentations
Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, and Tom Goldstein · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
A Dosovitskiy, L Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, M Dehghani, Matthias Minderer, G Heigold, S Gelly, Jakob Uszkoreit, and N Houlsby · 2021
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How well do feature visualizations support causal understanding of cnn activations?
Roland S Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas Wallis, and Wieland Brendel · 2021
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Making sense of dependence: Efficient black-box explanations using dependence measure
Paul Novello, Thomas Fel, and David Vigouroux · 2022
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HIVE: Evaluating the human interpretability of visual explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, and Olga Russakovsky · 2022
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Do users benefit from interpretable vision? a user study, baseline, and dataset
Leon Sixt, Martin Schuessler, Oana-Iuliana Popescu, Philipp Weiß, and Tim Landgraf · 2022
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Towards better understanding attribution methods
Sukrut Rao, Moritz Böhle, and Bernt Schiele · 2022
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What do vision transformers learn? a visual exploration
Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, and Tom Goldstein · 2022
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Flexivit: One model for all patch sizes
Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, and Filip Pavetic · 2022
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Craft: Concept recursive activation factorization for explainability
Thomas Fel, Agustin Picard, Louis Bethune, Thibaut Boissin, David Vigouroux, Julien Colin, Rémi Cadène, and Thomas Serre · 2022
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Guangrun Wang and Philip HS Torr · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Xplique: A deep learning explainability toolbox
Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poche, Justin Plakoo, Remi Cadene, Mathieu Chalvidal, Julien Colin, Thibaut Boissin, Louis Bethune, Agustin Picard, Claire Nicodeme, Laurent Gardes, Gregory Flandin, and Thomas Serre · 2022
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Don’t lie to me! robust and efficient explainability with verified perturbation analysis
Thomas Fel, Melanie Ducoffe, David Vigouroux, Remi Cadene, Mikael Capelle, Claire Nicodeme, and Thomas Serre · 2023
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On the coalitional decomposition of parameters of interest, 2023
Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, and Jean-Michel Loubes · 2023
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Diffusion models as artists: Are we closing the gap between humans and machines?
Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin, and Thomas Serre · 2023
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Don’t trust your eyes: on the (un)reliability of feature visualizations, 2023
Robert Geirhos, Roland S. Zimmermann, Blair Bilodeau, Wieland Brendel, and Been Kim · 2023
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