Fetching the paper…
Reading the bibliography…
Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging.
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.
Scope of validity of psnr in image/video quality assessment
Quan Huynh-Thu and Mohammed Ghanbari · 2008
Earlier work this paper cites.
Prediction, cognition and the brain
Andreja Bubic, D Yves Von Cramon, and Ricarda I Schubotz · 2010
Earlier work this paper cites.
Learning predictive models of a depth camera & manipulator from raw execution traces
Byron Boots, Arunkumar Byravan, and Dieter Fox · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra · 2014
Earlier work this paper cites.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
Earlier work this paper cites.
Anticipating the future by watching unlabeled video
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2015
Earlier work this paper cites.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Earlier work this paper cites.
Dynamic filter networks
Bert De Brabandere, Xu Jia, Tinne Tuytelaars, and Luc Van Gool · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Cited alongside, same era.
Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao, Evan W Archer, Liam Paninski, and John P Cunningham · 2016
Cited alongside, same era.
Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Cited alongside, same era.
Speed/accuracy trade-offs for modern convolutional object detectors
Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, et al · 2016
Unsupervised learning of disentangled representations from video
Emily Denton and Vighnesh Birodkar · 2017
Closest in time.
Self-Supervised Visual Planning with Temporal Skip Connections
Frederik Ebert, Chelsea Finn, Alex X. Lee, and Sergey Levine · 2017
Closest in time.
Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
Closest in time.
Motion prediction under multimodality with conditional stochastic networks
Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco, and Rahul Sukthankar · 2017
Closest in time.
Video pixel networks
Nal Kalchbrenner, Aäron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Composing graphical models with neural networks for structured representations and fast inference
Matthew Johnson, David K Duvenaud, Alex Wiltschko, Ryan P Adams, and Sandeep R Datta · 2016
Cited alongside, same era.
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2016
Cited alongside, same era.
Stochastic video prediction with conditional density estimation
Rui Shu, James Brofos, Frank Zhang, Hung Hai Bui, Mohammad Ghavamzadeh, and Mykel Kochenderfer · 2016
Cited alongside, same era.
Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
Cited alongside, same era.
An uncertain future: Forecasting from static images using variational autoencoders
Jacob Walker, Carl Doersch, Abhinav Gupta, and Martial Hebert · 2016
Cited alongside, same era.
Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
Tianfan Xue, Jiajun Wu, Katherine Bouman, and Bill Freeman · 2016
Cited alongside, same era.
Video imagination from a single image with transformation generation
Baoyang Chen, Wenmin Wang, Jinzhuo Wang, Xiongtao Chen, and Weimian Li · 2017
Cited alongside, same era.
Rahul G Krishnan, Uri Shalit, and David Sontag · 2017
Closest in time.
Yitong Li, Martin Renqiang Min, Dinghan Shen, David Carlson, and Lawrence Carin · 2017
Closest in time.
Video frame synthesis using deep voxel flow
Ziwei Liu, Raymond Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala · 2017
Closest in time.
Deep predictive coding networks for video prediction and unsupervised learning
William Lotter, Gabriel Kreiman, and David Cox · 2017
Closest in time.
Parallel multiscale autoregressive density estimation
Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, and Nando de Freitas · 2017
Closest in time.
Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2017
Closest in time.
Generating the future with adversarial transformers
Carl Vondrick and Antonio Torralba · 2017
Closest in time.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
Closest in time.