Fetching the paper…
Reading the bibliography…
The classification performance of deep neural networks has begun to asymptote at near-perfect levels.
Semantic hierarchies for visual object recognition
M. Marszalek and C. Schmid · 2007
Earlier work this paper cites.
Sharing visual features for multiclass and multiview object detection
Antonio Torralba, Kevin P Murphy, and William T Freeman · 2007
Earlier work this paper cites.
Exploiting object hierarchy: combining models from different category levels
A. Zweig and D. Weinshall · 2007
Earlier work this paper cites.
Learning and using taxonomies for fast visual categorization
G. Griffin and P. Perona · 2008
Earlier work this paper cites.
Women, fire, and dangerous things
George Lakoff · 2008
Earlier work this paper cites.
80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Visual recognition with humans in the loop
Steve Branson, Catherine Wah, Florian Schroff, Boris Babenko, Peter Welinder, Pietro Perona, and Serge Belongie · 2010
Earlier work this paper cites.
Semantic label sharing for learning with many categories
R. Fergus, H. Bernal, Y. Weiss, and A. Torralba · 2010
Earlier work this paper cites.
Amazon’s mechanical turk: A new source of inexpensive, yet high-quality, data?
Michael Buhrmester, Tracy Kwang, and Samuel D Gosling · 2011
Earlier work this paper cites.
Sharing features between objects and their attributes
S.J. Hwang, F. Sha, and K. Grauman · 2011
Earlier work this paper cites.
Maximum margin multi-label structured prediction
C.H. Lampert · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba, Alexei A Efros, et al · 2011
Earlier work this paper cites.
Crowdsourcing multi-label classification for taxonomy creation
Jonathan Bragg, Mausam, and Daniel S. Weld · 2013
Earlier work this paper cites.
Cascade: Crowdsourcing taxonomy creation
L. B. Chilton, G. Little, D. Edge, D. S. Weld, and J. A. Landay · 2013
Earlier work this paper cites.
Devise: A deep visual-semantic embedding model
A. Frome, G.S. Corrado, J. Shlens, S. Bengio, J. Dean, and T. Mikolov · 2013
Earlier work this paper cites.
Visual concept learning: Combining machine vision and bayesian generalization on concept hierarchies
Y. Jia, J.T. Abbott, J. Austerweil, T. Griffiths, and T. Darrell · 2013
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 · 2013
Earlier work this paper cites.
Large-scale object classification using label relation graphs
Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, and Hartwig Adam · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Cited alongside, same era.
Learning classification models with soft-label information
Quang Nguyen, Hamed Valizadegan, and Milos Hauskrecht · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Xavier Gastaldi · 2017
Later among the works it cites.
Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
Later among the works it cites.
Deep pyramidal residual networks
Dongyoon Han, Jiwhan Kim, and Junmo Kim · 2017
Later among the works it cites.
Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory F. Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
Later among the works it cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Cited alongside, same era.
Combining crowd and expert labels using decision theoretic active learning
An Thanh Nguyen, Byron C Wallace, and Matthew Lease · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Crowdsourcing in Computer Vision
Adriana Kovashka, Olga Russakovsky, Li Fei-Fei, and Kristen Grauman · 2016
Cited alongside, same era.
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Li Fei-Fei · 2017
Later among the works it cites.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
Deep learning is robust to massive label noise
David Rolnick, Andreas Veit, Serge J. Belongie, and Nir Shavit · 2017
Later among the works it cites.
Toward robustness against label noise in training deep discriminative neural networks
Arash Vahdat · 2017
Later among the works it cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross B Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Later among the works it cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Later among the works it cites.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Later among the works it cites.
The moral machine experiment
Edmond Awad, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Henrich, Azim Shariff, Jean-François Bonnefon, and Iyad Rahwan · 2018
Later among the works it cites.
Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
Later among the works it cites.
CINIC-10 is not imagenet or CIFAR-10
Luke Nicholas Darlow, Elliot J. Crowley, Antreas Antoniou, and Amos J. Storkey · 2018
Later among the works it cites.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, and Zhifeng Chen · 2018
Later among the works it cites.
Do cifar-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
Later among the works it cites.