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Deep-learning models can extract a rich assortment of features from data.
The importance of shape in early lexical learning
Barbara Landau, Linda B Smith, and Susan S Jones · 1988
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Kernel methods for deep learning
Youngmin Cho and Lawrence Saul · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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In search of the real inductive bias: On the role of implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 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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Generating captions without looking beyond objects
Hendrik Heuer, Christof Monz, and Arnold WM Smeulders · 2016
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Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 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
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Deep learning generalizes because the parameter-function map is biased towards simple functions
Guillermo Valle Pérez, Chico Q Camargo, and Ard A Louis · 2018
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Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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On the learning dynamics of deep neural networks
Remi Tachet, Mohammad Pezeshki, Samira Shabanian, Aaron Courville, and Yoshua Bengio · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Implicit regularization in deep matrix factorization
Sanjeev Arora, Nadav Cohen, Wei Hu, and Yuping Luo · 2019
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Unmasking clever hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2019
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Noise or signal: The role of image backgrounds in object recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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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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Understanding the failure modes of out-of-distribution generalization
Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur · 2021
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Gradient starvation: A learning proclivity in neural networks
Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C Courville, Doina Precup, and Guillaume Lajoie · 2021
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Can contrastive learning avoid shortcut solutions?
Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, and Suvrit Sra · 2021
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R Thomas McCoy, Ellie Pavlick, and Tal Linzen · 2019
Cited alongside, same era.
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
Cited alongside, same era.
On the structural sensitivity of deep convolutional networks to the directions of fourier basis functions
Yusuke Tsuzuku and Issei Sato · 2019
Cited alongside, same era.
A fourier perspective on model robustness in computer vision
Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, and Justin Gilmer · 2019
Cited alongside, same era.
Local features and global shape information in object classification by deep convolutional neural networks
Nicholas Baker, Hongjing Lu, Gennady Erlikhman, and Philip J Kellman · 2020
Cited alongside, same era.
Deep equals shallow for relu networks in kernel regimes
Alberto Bietti and Francis R. Bach · 2020
Cited alongside, same era.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
Cited alongside, same era.
Chaitanya K Ryali, David J Schwab, and Ari S Morcos · 2021
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2022
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A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes
Mazda Moayeri, Phillip Pope, Yogesh Balaji, and Soheil Feizi · 2022
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Salient imagenet: How to discover spurious features in deep learning?
Sahil Singla and Soheil Feizi · 2022
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A developmentally-inspired examination of shape versus texture bias in machines
Alexa R Tartaglini, Wai Keen Vong, and Brenden M Lake · 2022
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Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization
Damien Teney, Ehsan Abbasnejad, Simon Lucey, and Anton Van den Hengel · 2022
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Signal strength and noise drive feature preference in cnn image classifiers
Max Wolff and Stuart Wolff · 2022
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Enhancing representation learning with deep classifiers in presence of shortcut
Amirhossein Ahmadian and Fredrik Lindsten · 2023
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A holistic approach to unifying automatic concept extraction and concept importance estimation
Thomas Fel, Victor Boutin, Mazda Moayeri, Rémi Cadène, Louis Bethune, Mathieu Chalvidal, Thomas Serre, et al · 2023
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Towards last-layer retraining for group robustness with fewer annotations
Tyler LaBonte, Vidya Muthukumar, and Abhishek Kumar · 2023
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Don’t blame dataset shift! shortcut learning due to gradients and cross entropy
Aahlad Puli, Lily Zhang, Yoav Wald, and Rajesh Ranganath · 2023
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Spatial-frequency channels, shape bias, and adversarial robustness
Ajay Subramanian, Elena Sizikova, Najib J Majaj, and Denis G Pelli · 2023
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Sifer: Overcoming simplicity bias in deep networks using a feature sieve
Rishabh Tiwari and Pradeep Shenoy · 2023
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