Fetching the paper…
Reading the bibliography…
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems.
ALVINN: An autonomous land vehicle in a neural network
D. Pomerleau · 1989
Earlier work this paper cites.
Off-road obstacle avoidance through end-to-end learning
Y. LeCun, U. Muller, J. Ben, E. Cosatto, and B. Flepp · 2005
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and D. Bagnell · 2011
Earlier work this paper cites.
The ecological approach to visual perception: classic edition
J. J. Gibson · 2014
Earlier work this paper cites.
DeepDriving: Learning affordance for direct perception in autonomous driving
C. Chen, A. Seff, A. L. Kornhauser, and J. Xiao · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
M. Bojarski, D. D. Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba · 2016
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Earlier work this paper cites.
Shuffle and learn: unsupervised learning using temporal order verification
I. Misra, C. L. Zitnick, and M. Hebert · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
Earlier work this paper cites.
End-to-end learning of driving models from large-scale video datasets
H. Xu, Y. Gao, F. Yu, and T. Darrell · 2017
Earlier work this paper cites.
CARLA: An open urban driving simulator
A. Dosovitskiy, G. Ros, F. Codevilla, A. López, and V. Koltun · 2017
Earlier work this paper cites.
Colorization as a proxy task for visual understanding
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
Earlier work this paper cites.
Loss is its own reward: Self-supervision for reinforcement learning
E. Shelhamer, P. Mahmoudieh, M. Argus, and T. Darrell · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
Cited alongside, same era.
Understanding intermediate layers using linear classifier probes
G. Alain and Y. Bengio · 2017
Cited alongside, same era.
End-to-end learning of driving models with surround-view cameras and route planners
S. Hecker, D. Dai, and L. Van Gool · 2018
Cited alongside, same era.
Variational autoencoder for end-to-end control of autonomous driving with novelty detection and training de-biasing
A. Amini, W. Schwarting, G. Rosman, B. Araki, S. Karaman, and D. Rus · 2018
Cited alongside, same era.
End-to-end driving via conditional imitation learning
A survey of deep learning techniques for autonomous driving
S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu · 2019
Later among the works it cites.
Exploring the limitations of behavior cloning for autonomous driving
F. Codevilla, E. Santana, A. M. López, and A. Gaidon · 2019
Later among the works it cites.
Unsupervised state representation learning in Atari
A. Anand, E. Racah, S. Ozair, Y. Bengio, M.-A. Côté, and R. D. Hjelm · 2019
Later among the works it cites.
Self-supervised representation learning by rotation feature decoupling
Z. Feng, C. Xu, and D. Tao · 2019
Later among the works it cites.
Self-supervised domain adaptation for computer vision tasks
J. Xu, L. Xiao, and A. López · 2019
Later among the works it cites.
Revisiting self-supervised visual representation learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Codevilla, M. Müller, A. M. López, V. Koltun, and A. Dosovitskiy · 2018
Cited alongside, same era.
Causal confusion in imitation learning
P. Hamm, D. Jayaraman, and S. Levine · 2018
Cited alongside, same era.
Conditional affordance learning for driving in urban environments
A. Sauer, N. Savinov, and A. Geiger · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
Cited alongside, same era.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
K. Chua, R. Calandra, R. McAllister, and S. Levine · 2018
Cited alongside, same era.
A survey of autonomous driving: Common practices and emerging technologies
E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda · 2019
Cited alongside, same era.
A. Kolesnikov, X. Zhai, and L. Beyer · 2019
Later among the works it cites.
Scaling and benchmarking self-supervised visual representation learning
P. Goyal, D. Mahajan, A. Gupta, and I. Misra · 2019
Later among the works it cites.
Learning latent dynamics for planning from pixels
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson · 2019
Later among the works it cites.
Learning by cheating
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl · 2019
Later among the works it cites.
Lates: Latent space distillation for teacher-student driving policy learning
A. Zhao, T. He, Y. Liang, H. Huang, G. V. den Broeck, and S. Soatto · 2019
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2019
Later among the works it cites.
A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
Closest in time.