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What spatial frequency information do humans and neural networks use to recognize objects? In neuroscience, critical band masking is an established tool that can reveal the frequency-selective filters used for object recognition.
Auditory patterns
Harvey Fletcher · 1940
Earlier work this paper cites.
Application of fourier analysis to the visibility of gratings
Fergus W Campbell and John G Robson · 1968
Earlier work this paper cites.
On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images
Colin Blakemore and Fergus W Campbell · 1969
Earlier work this paper cites.
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
Kunihiko Fukushima and Sei Miyake · 1982
Earlier work this paper cites.
The laplacian pyramid as a compact image code
Peter J Burt and Edward H Adelson · 1987
Earlier work this paper cites.
Object spatial frequencies, retinal spatial frequencies, noise, and the efficiency of letter discrimination
David H Parish and George Sperling · 1991
Earlier work this paper cites.
Syntactic context and the shape bias in children’s and adults’ lexical learning
Barbara Landau, Linda B Smith, and Susan Jones · 1992
Earlier work this paper cites.
The visual filter mediating letter identification
Joshua A Solomon and Denis G Pelli · 1994
Earlier work this paper cites.
Wordnet: a lexical database for english
George A Miller · 1995
Earlier work this paper cites.
Hierarchical models of object recognition in cortex
Maximilian Riesenhuber and Tomaso Poggio · 1999
Earlier work this paper cites.
The role of spatial frequency channels in letter identification
Najib J Majaj, Denis G Pelli, Peri Kurshan, and Melanie Palomares · 2002
Earlier work this paper cites.
Representational similarity analysis-connecting the branches of systems neuroscience
Nikolaus Kriegeskorte, Marieke Mur, and Peter A Bandettini · 2008
Earlier work this paper cites.
Scale dependence and channel switching in letter identification
Ipek Oruç and Michael S Landy · 2009
Earlier work this paper cites.
Critical frequencies in the perception of letters, faces, and novel shapes: Evidence for limited scale invariance for faces
İpek Oruç and Jason JS Barton · 2010
Earlier work this paper cites.
Torchvision the machine-vision package of torch
Sébastien Marcel and Yann Rodriguez · 2010
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Deep supervised, but not unsupervised, models may explain it cortical representation
Seyed-Mahdi Khaligh-Razavi and Nikolaus Kriegeskorte · 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 Bernstein, et al · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Earlier work this paper cites.
" just another tool for online studies”(jatos): An easy solution for setup and management of web servers supporting online studies
Kristian Lange, Simone Kühn, and Elisa Filevich · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Using goal-driven deep learning models to understand sensory cortex
Daniel LK Yamins and James J DiCarlo · 2016
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos RM Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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Deep convolutional networks do not classify based on global object shape
Nicholas Baker, Hongjing Lu, Gennady Erlikhman, and Philip J Kellman · 2018
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
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Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
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Brain-score: Which artificial neural network for object recognition is most brain-like?
Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J Majaj, Rishi Rajalingham, Elias B Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Franziska Geiger, et al · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Adversarial robustness toolbox v1. 0.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, et al · 2018
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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A fourier perspective on model robustness in computer vision
Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, and Justin Gilmer · 2019
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Humans can decipher adversarial images
Zhenglong Zhou and Chaz Firestone · 2019
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Are convolutional neural networks or transformers more like human vision?
Shikhar Tuli, Ishita Dasgupta, Erin Grant, and Thomas L Griffiths · 2021
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Dissecting the high-frequency bias in convolutional neural networks
Antonio A Abello, Roberto Hirata, and Zhangyang Wang · 2021
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Impact of spatial frequency based constraints on adversarial robustness
Rémi Bernhard, Pierre-Alain Moëllic, Martial Mermillod, Yannick Bourrier, Romain Cohendet, Miguel Solinas, and Marina Reyboz · 2021
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A frequency perspective of adversarial robustness
Shishira R Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, and Abhinav Shrivastava · 2021
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Partial success in closing the gap between human and machine vision
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2021
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lab. js: A free, open, online study builder
Felix Henninger, Yury Shevchenko, Ulf K Mertens, Pascal J Kieslich, and Benjamin E Hilbig · 2021
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Frequency shortcut learning in neural networks
Shunxin Wang, Raymond Veldhuis, Christoph Brune, and Nicola Strisciuglio · 2022
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Model metamers illuminate divergences between biological and artificial neural networks
Jenelle Feather, Guillaume Leclerc, Aleksander Mądry, and Josh H McDermott · 2022
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Sensitivity to naturalistic texture relies primarily on high spatial frequencies
Justin D Lieber, Gerick M Lee, Najib J Majaj, and J Anthony Movshon · 2023
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Robust deep learning object recognition models rely on low frequency information in natural images
Zhe Li, Josue Ortega Caro, Evgenia Rusak, Wieland Brendel, Matthias Bethge, Fabio Anselmi, Ankit B Patel, Andreas S Tolias, and Xaq Pitkow · 2023
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An extended study of human-like behavior under adversarial training
Paul Gavrikov, Janis Keuper, and Margret Keuper · 2023
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Subtle adversarial image manipulations influence both human and machine perception
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Scaling vision transformers to 22 billion parameters
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