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Unsupervised pre-training methods utilizing large and diverse datasets have achieved tremendous success across a range of domains.
Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat
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Human3.6M: Large scale datasets and predictive methods for 3d human sensing in natural environments
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Adam: A method for stochastic optimization
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 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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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2016
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Unsupervised learning of disentangled representations from video
Emily Denton and Vighnesh Birodkar · 2017
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CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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The "something something" video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Coarse-to-fine volumetric prediction for single-image 3d human pose
Georgios Pavlakos, Xiaowei Zhou, Konstantinos G Derpanis, and Kostas Daniilidis · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Decomposing motion and content for natural video sequence prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2017
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Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano Soatto · 2018
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Playing hard exploration games by watching youtube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, and Nando De Freitas · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Disentangled sequential autoencoder
Yingzhen Li and Stephan Mandt · 2018
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Imitation from observation: Learning to imitate behaviors from raw video via context translation
YuXuan Liu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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DeepMDP: Learning continuous latent space models for representation learning
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, and Marc G Bellemare · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Concept2Robot: Learning manipulation concepts from instructions and human demonstrations
Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, and Jeannette Bohg · 2021
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Mastering atari games with limited data
Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, and Yang Gao · 2021
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Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine · 2021
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Video pretraining (vpt): Learning to act by watching unlabeled online videos
Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune · 2022
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HowTo100M: Learning a text-video embedding by watching hundred million narrated video clips
Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Semantic visual navigation by watching youtube videos
Matthew Chang, Arjun Gupta, and Saurabh Gupta · 2020
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Stochastic latent residual video prediction
Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen, Sylvain Lamprier, and Patrick Gallinari · 2020
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Measuring visual generalization in continuous control from pixels
Jake Grigsby and Yanjun Qi · 2020
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Chang Chen, Yi-Fu Wu, Jaesik Yoon, and Sungjin Ahn · 2022
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DreamerPro: Reconstruction-free model-based reinforcement learning with prototypical representations
Fei Deng, Ingook Jang, and Sungjin Ahn · 2022
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Ego4D: Around the world in 3,000 hours of egocentric video
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al · 2022
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Temporal difference learning for model predictive control
Nicklas Hansen, Xiaolong Wang, and Hao Su · 2022
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Simple but effective: Clip embeddings for embodied ai
Apoorv Khandelwal, Luca Weihs, Roozbeh Mottaghi, and Aniruddha Kembhavi · 2022
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A path towards autonomous machine intelligence
Yann LeCun · 2022
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R3M: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2022
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Iso-Dream: Isolating and leveraging noncontrollable visual dynamics in world models
Minting Pan, Xiangming Zhu, Yunbo Wang, and Xiaokang Yang · 2022
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The unsurprising effectiveness of pre-trained vision models for control
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, and Abhinav Gupta · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Decoupled reinforcement learning to stabilise intrinsically-motivated exploration
Lukas Schäfer, Filippos Christianos, Josiah P Hanna, and Stefano V Albrecht · 2022
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Masked world models for visual control
Younggyo Seo, Danijar Hafner, Hao Liu, Fangchen Liu, Stephen James, Kimin Lee, and Pieter Abbeel · 2022
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Reinforcement learning with action-free pre-training from videos
Younggyo Seo, Kimin Lee, Stephen L James, and Pieter Abbeel · 2022
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Masked visual pre-training for motor control
Tete Xiao, Ilija Radosavovic, Trevor Darrell, and Jitendra Malik · 2022
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Mastering visual continuous control: Improved data-augmented reinforcement learning
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto · 2022
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Pre-trained image encoder for generalizable visual reinforcement learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, Yi Wu, Yang Gao, and Huazhe Xu · 2022
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Learning to drive by watching youtube videos: Action-conditioned contrastive policy pretraining
Qihang Zhang, Zhenghao Peng, and Bolei Zhou · 2022
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Mastering diverse domains through world models
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Where are we in the search for an artificial visual cortex for embodied intelligence?
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Structured world models from human videos
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Transformer-based world models are happy with 100k interactions
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On the feasibility of cross-task transfer with model-based reinforcement learning
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Become a proficient player with limited data through watching pure videos
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