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Recent strategies achieved ensembling "for free" by fitting concurrently diverse subnetworks inside a single base network.
Neural network ensembles
Lars Kai Hansen and Peter Salamon · 1990
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Optimal brain damage
Yann Lecun, J. S. Denker, Sara A. Solla, R. E. Howard, and L.D. Jackel · 1990
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When networks disagree: Ensemble methods for hybrid neural networks
Michael Perrone and Leon Cooper · 1993
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Bagging predictors
Leo Breiman · 1996
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Multitask learning
Rich Caruana · 1997
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Popular ensemble methods: An empirical study
David Opitz and Richard Maclin · 1999
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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The information bottleneck method
Naftali Tishby · 2001
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Comparison of classifier selection methods for improving committee performance
Matti Aksela · 2003
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Why M heads are better than one: Training a diverse ensemble of deep networks
Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David J. Crandall, and Dhruv Batra · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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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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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Residual networks behave like ensembles of relatively shallow networks
Andreas Veit, Michael Wilber, and Serge Belongie · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Pruning convolutional neural networks for resource efficient transfer learning
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2017
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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2018
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Dropblock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2018
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Data augmentation by pairing samples for images classification
Hiroshi Inoue · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Understanding mixup training methods
Daojun Liang, Feng Yang, Tian Zhang, and Peter Yang · 2018
Deep ensembles on a fixed memory budget: One wide network or several thinner ones?
Nadezhda Chirkova, Ekaterina Lobacheva, and Dmitry P. Vetrov · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, and Quoc Le · 2020
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Masksembles for uncertainty estimation
Nikita Durasov, Timur Bagautdinov, Pierre Baque, and Pascal Fua · 2020
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Efficient and scalable bayesian neural nets with rank-1 factors
Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
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Patchup: A regularization technique for convolutional neural networks
Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan, Vikas Verma, and Sarath Chandar · 2020
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Milking cowmask for semi-supervised image classification
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Cited alongside, same era.
A modern take on the bias-variance tradeoff in neural networks
Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, and Ioannis Mitliagkas · 2018
Cited alongside, same era.
Between-class learning for image classification
Yuji Tokozume, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Cited alongside, same era.
Learning from between-class examples for deep sound recognition
Yuji Tokozume, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Geoff French, Avital Oliver, and Tim Salimans · 2020
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Evaluating scalable bayesian deep learning methods for robust computer vision
Fredrik K Gustafsson, Martin Danelljan, and Thomas B Schon · 2020
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Fmix: Enhancing mixed sample data augmentation
Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, and Jonathon Hare · 2020
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Augment your batch: Improving generalization through instance repetition
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry · 2020
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Puzzle mix: Exploiting saliency and local statistics for optimal mixup
Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song · 2020
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On feature normalization and data augmentation
Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, and Kilian Q. Weinberger · 2020
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On power laws in deep ensembles
Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, and Dmitry P Vetrov · 2020
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Affinity and diversity: Quantifying mechanisms of data augmentation
Raphael Gontijo Lopes, Sylvia J. Smullin, Ekin D. Cubuk, and Ethan Dyer · 2020
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Optimal lottery tickets via subsetsum: Logarithmic over-parameterization is sufficient
Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, and Dimitris Papailiopoulos · 2020
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Resizemix: Mixing data with preserved object information and true labels
Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, and Xinggang Wang · 2020
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Aggregated learning: A vector-quantization approach to learning neural network classifiers
Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh, Yongyi Mao, and Richong Zhang · 2020
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Data augmentation using random image cropping and patching for deep cnns
Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara · 2020
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Multiple networks are more efficient than one: Fast and accurate models via ensembles and cascades
Xiaofang Wang, Dan Kondratyuk, Kris M. Kitani, Yair Movshovitz-Attias, and Elad Eban · 2020
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Mothernets: Rapid deep ensemble learning
Abdul Wasay, Brian Hentschel, Yuze Liao, Sanyuan Chen, and Stratos Idreos · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
Andrew Gordon Wilson and Pavel Izmailov · 2020
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Gradaug: A new regularization method for deep neural networks
Taojiannan Yang, Sijie Zhu, and Chen Chen · 2020
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Splitnet: Divide and co-training
Shuai Zhao, Liguang Zhou, Wenxiao Wang, Deng Cai, Tin Lun Lam, and Yangsheng Xu · 2020
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Gridmix: Strong regularization through local context mapping
Kyungjune Baek, Duhyeon Bang, and Hyunjung Shim · 2021
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Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Liu, Jasper Roland Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, and Dustin Tran · 2021
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Co-mixup: Saliency guided joint mixup with supermodular diversity
JangHyun Kim, Wonho Choo, Hosan Jeong, and Hyun Oh Song · 2021
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Dice: Diversity in deep ensembles via conditional redundancy adversarial estimation
Alexandre Rame and Matthieu Cord · 2021
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Saliencymix: A saliency guided data augmentation strategy for better regularization
A. F. M. Shahab Uddin, Mst. Sirazam Monira, Wheemyung Shin, TaeChoong Chung, and Sung-Ho Bae · 2021
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Combining ensembles and data augmentation can harm your calibration
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, and Dustin Tran · 2021
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How does mixup help with robustness and generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, and James Zou · 2021
Closest in time.