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Recent work has uncovered a striking phenomenon in large-capacity neural networks: they contain blocks of contiguous hidden layers with highly similar representations.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Algorithms for learning kernels based on centered alignment
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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Feature selection via dependence maximization
Le Song, Alex Smola, Arthur Gretton, Justin Bedo, and Karsten Borgwardt · 2012
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Some improvements on deep convolutional neural network based image classification
Andrew G Howard · 2013
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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"Why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Deep learning for identifying metastatic breast cancer
Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, and Andrew H Beck · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Xavier Gastaldi · 2017
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Measuring the tendency of CNNs to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
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Detecting cancer metastases on gigapixel pathology images
Yun Liu, Krishna Gadepalli, Mohammad Norouzi, George E Dahl, Timo Kohlberger, Aleksey Boyko, Subhashini Venugopalan, Aleksei Timofeev, Philip Q Nelson, Greg S Corrado, et al · 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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Deep convolutional networks do not classify based on global object shape
Nicholas Baker, Hongjing Lu, Gennady Erlikhman, and Philip J Kellman · 2018
Cited alongside, same era.
To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, and Soumik Mandal · 2018
Cited alongside, same era.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
Cited alongside, same era.
A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation
Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
Cited alongside, same era.
Semantic adversarial examples
Hossein Hosseini and Radha Poovendran · 2018
Cited alongside, same era.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R Thomas McCoy, Ellie Pavlick, and Tal Linzen · 2019
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Sgd on neural networks learns functions of increasing complexity
Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L Edelman, Fred Zhang, and Boaz Barak · 2019
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Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
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Probing the state of the art: A critical look at visual representation evaluation
Cinjon Resnick, Zeping Zhan, and Joan Bruna · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Insights on representational similarity in neural networks with canonical correlation
Ari S Morcos, Maithra Raghu, and Samy Bengio · 2018
Cited alongside, same era.
Deep learning generalizes because the parameter-function map is biased towards simple functions
Guillermo Valle-Perez, Chico Q Camargo, and Ard A Louis · 2018
Cited alongside, same era.
Rotation equivariant cnns for digital pathology
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
Cited alongside, same era.
Identifying and controlling important neurons in neural machine translation
Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass · 2019
Cited alongside, same era.
Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
Cited alongside, same era.
The origins and prevalence of texture bias in convolutional neural networks
Katherine L Hermann, Ting Chen, and Simon Kornblith · 2019
Cited alongside, same era.
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Jessica AF Thompson, Yoshua Bengio, and Marc Schoenwiesner · 2019
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High-dimensional dynamics of generalization error in neural networks
Madhu S Advani, Andrew M Saxe, and Haim Sompolinsky · 2020
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Benign overfitting in linear regression
Peter L Bartlett, Philip M Long, Gábor Lugosi, and Alexander Tsigler · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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What shapes feature representations? exploring datasets, architectures, and training
Katherine L Hermann and Andrew K Lampinen · 2020
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Just interpolate: Kernel “ridgeless” regression can generalize
Tengyuan Liang, Alexander Rakhlin, et al · 2020
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What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
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The low-rank simplicity bias in deep networks
Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, and Phillip Isola · 2021
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Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth
Thao Nguyen, Maithra Raghu, and Simon Kornblith · 2021
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