Fetching the paper…
Reading the bibliography…
Deep Learning heavily depends on large labeled datasets which limits further improvements.
Probability of error of some adaptive pattern-recognition machines
H Scudder · 1965
Earlier work this paper cites.
Automatically generating extraction patterns from untagged text
Ellen Riloff · 1996
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
Earlier work this paper cites.
Learning extraction patterns for subjective expressions
Ellen Riloff and Janyce Wiebe · 2003
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
Earlier work this paper cites.
Semi-supervised learning literature survey
Xiaojin Jerry Zhu · 2005
Earlier work this paper cites.
Semi-Supervised Learning
O. Chapelle, B. Schölkopf, and A. Zien · 2006
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 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 and Geoffrey Hinton · 2009
Earlier work this paper cites.
Introduction to semi-supervised learning
Xiaojin Zhu and Andrew B Goldberg · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
Earlier work this paper cites.
Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, , S. Ren, and J. Sun · 2016
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Cited alongside, same era.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
Cited alongside, same era.
Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Cited alongside, same era.
There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2019
Later among the works it cites.
Learning and the unknown: Surveying steps toward openworld recognition
T. E. Boult, S. Cruz, A.R. Dhamija, M. Gunther, J. Henrydoss, and W.J. Scheirer · 2019
Later among the works it cites.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
Later among the works it cites.
Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Later among the works it cites.
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
M. Hein, M. Andriushchenko, and J. Bitterwolf · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Weinberger · 2017
Cited alongside, same era.
Deep pyramidal residual networks
Dongyoon Han, Jiwhan Kim, and Junmo Kim · 2017
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, H. Lee, K. Lee, and J. Shin · 2018
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
S. Liang, Y. Li, and R. Srikant · 2018
Cited alongside, same era.
Dan Hendrycks and Thomas Dietterich · 2019
Later among the works it cites.
Deep anomaly detection with outlier exposure
D. Hendrycks, M. Mazeika, and T. Dietterich · 2019
Later among the works it cites.
Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
Later among the works it cites.
Outlier exposure with confidence control for out-of-distribution detection
Aristotelis-Angelos Papadopoulos, Mohammad Reza Rajati, Nazim Shaikh, and Jiamian Wang · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
Later among the works it cites.
Billion-scale semi-supervised learning for image classification
I Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 2019
Later among the works it cites.
Shakedrop regularization for deep residual learning
Yoshihiro Yamada, Masakazu Iwamura, Takuya Akiba, and Koichi Kise · 2019
Later among the works it cites.
Semi-supervised learning under class distribution mismatch
Yanbei Chen, Xiatian Zhu, Wei Li, and Shaogang Gong · 2020
Closest in time.
Safe deep semi-supervised learning for unseen-class unlabeled data
Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yu-Feng Li, and Zhi-Hua Zhou · 2020
Closest in time.
Fmix: Enhancing mixed sample data augmentation
Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, and Adam Prügel-Bennett Jonathon Hare · 2020
Closest in time.
Large image datasets: A pyrrhic win for computer vision?
Vinay Uday Prabhu and Abeba Birhane · 2020
Closest in time.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
Closest in time.
Multi-task curriculum framework for open-set semi-supervised learning
Qing Yu, Daiki Ikami, Go Irie, and Kiyoharu Aizawa · 2020
Closest in time.