Fetching the paper…
Reading the bibliography…
Despite being able to capture a range of features of the data, high accuracy models trained with supervision tend to make similar predictions.
A simple weight decay can improve generalization
Anders Krogh and John A Hertz · 1992
Earlier work this paper cites.
When networks disagree: Ensemble methods for hybrid neural networks
Michael P Perrone and Leon N Cooper · 1992
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 1992
Earlier work this paper cites.
Stacked generalization
David H Wolpert · 1992
Earlier work this paper cites.
Experiments with a new boosting algorithm
Yoav Freund, Robert E Schapire, et al · 1996
Earlier work this paper cites.
Popular ensemble methods: An empirical study
David Opitz and Richard Maclin · 1999
Earlier work this paper cites.
Exploiting open-endedness to solve problems through the search for novelty
Joel Lehman and Kenneth O Stanley · 2008
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Densenet: Implementing efficient convnet descriptor pyramids
Forrest Iandola, Matt Moskewicz, Sergey Karayev, Ross Girshick, Trevor Darrell, and Kurt Keutzer · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, and Jiashi Feng · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Earlier work this paper cites.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Earlier work this paper cites.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Earlier work this paper cites.
Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
Earlier work this paper cites.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
Earlier work this paper cites.
Dropblock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2018
Cited alongside, same era.
Fix your classifier: the marginal value of training the last weight layer
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Cited alongside, same era.
Adversarial attacks and defences competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, et al · 2018
Cited alongside, same era.
Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Later among the works it cites.
Centermask: Real-time anchor-free instance segmentation
Youngwan Lee and Jongyoul Park · 2020
Later among the works it cites.
What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
Later among the works it cites.
Why are bootstrapped deep ensembles not better?
Jeremy Nixon, Balaji Lakshminarayanan, and Dustin Tran · 2020
Later among the works it cites.
Mitigating bias in calibration error estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, and Michael C Mozer · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Deep layer aggregation
Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell · 2018
Cited alongside, same era.
Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Cited alongside, same era.
Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
Cited alongside, same era.
Selective kernel networks
Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang · 2019
Cited alongside, same era.
Deep neural network ensembles against deception: Ensemble diversity, accuracy and robustness
Ling Liu, Wenqi Wei, Ka-Ho Chow, Margaret Loper, Emre Gursoy, Stacey Truex, and Yanzhao Wu · 2019
Cited alongside, same era.
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, and Florian Shkurti · 2020
Later among the works it cites.
Deep high-resolution representation learning for visual recognition
Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al · 2020
Later among the works it cites.
Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
Later among the works it cites.
Hyperparameter ensembles for robustness and uncertainty quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton · 2020
Later among the works it cites.
Exploring the limits of large scale pre-training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, and Hanie Sedghi · 2021
Closest in time.
The evolution of out-of-distribution robustness throughout fine-tuning
Anders Andreassen, Yasaman Bahri, Behnam Neyshabur, and Rebecca Roelofs · 2021
Closest in time.
Do self-supervised and supervised methods learn similar visual representations?, 2021
Tom George Grigg, Dan Busbridge, Jason Ramapuram, and Russ Webb · 2021
Closest in time.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V Le, Yunhsuan Sung, Zhen Li, and Tom Duerig · 2021
Closest in time.
Meta pseudo labels
Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V Le · 2021
Closest in time.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Closest in time.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Closest in time.
Neural ensemble search for uncertainty estimation and dataset shift
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C Holmes, Frank Hutter, and Yee Teh · 2021
Closest in time.
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2021
Closest in time.
Does your dermatology classifier know what it doesn’t know? detecting the long-tail of unseen conditions
Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zach Beaver, et al · 2022
Closest in time.