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We revisit and extend model stitching (Lenc & Vedaldi 2015) as a methodology to study the internal representations of neural networks.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
Earlier work this paper cites.
Canonical correlation analysis: An overview with application to learning methods
David R Hardoon, Sandor Szedmak, and John Shawe-Taylor · 2004
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Representational similarity analysis - connecting the branches of systems neuroscience
Nikolaus Kriegeskorte, Marieke Mur, and Peter Bandettini · 2008
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On the surprising similarities between supervised and self-supervised models
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2010
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Learning deep features for scene recognition using places database
Bolei Zhou, Àgata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 2014
Earlier work this paper cites.
Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Understanding image representations by measuring their equivariance and equivalence
Karel Lenc and Andrea Vedaldi · 2015
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Earlier work this paper cites.
Convergent learning: Do different neural networks learn the same representations?, 2016
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John Hopcroft · 2016
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Topology and geometry of half-rectified network optimization, 2017
C. Daniel Freeman and Joan Bruna · 2017
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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SVCCA: singular vector canonical correlation analysis for deep learning dynamics and interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
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Essentially no barriers in neural network energy landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred Hamprecht · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns, 2018
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Cited alongside, same era.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
Cited alongside, same era.
Insights on representational similarity in neural networks with canonical correlation
Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 2020
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Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Bad global minima exist and sgd can reach them
Shengchao Liu, Dimitris Papailiopoulos, and Dimitris Achlioptas · 2020
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Distributional generalization: A new kind of generalization
Preetum Nakkiran and Yamini Bansal · 2020
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An overview of early vision in inceptionv1
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter · 2020
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Ari S Morcos, Maithra Raghu, and Samy Bengio · 2018
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How to train your resnet
David Page · 2018
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Towards understanding learning representations: To what extent do different neural networks learn the same representation, 2018
Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, and John Hopcroft · 2018
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey E. Hinton · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
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Unsupervised learning of visual features by contrasting cluster assignments, 2021a
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin
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Emerging properties in self-supervised vision transformers, 2021b
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin
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Yamini Bansal, Gal Kaplun, and Boaz Barak · 2021
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Multimodal neurons in artificial neural networks
Gabriel Goh, Nick Cammarata †, Chelsea Voss †, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah · 2021
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The deep bootstrap framework: Good online learners are good offline generalizers, 2021
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Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth
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