Factorized Variational Autoencoders for Modeling Audience Reactions to Movies

By Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori. 2017

50 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Generative Models, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Bernhard Schölkopf
2. Luc Van Gool
3. Dacheng Tao
4. Trevor Darrell
5. Jitendra Malik
6. Francesco Locatello
7. Ming-Hsuan Yang
8. Xiaogang Wang

In Generative Models

1. Mario Lucic
2. Olivier Bachem
3. Michael Tschannen
4. Sylvain Gelly
5. Sjoerd van Steenkiste
6. Francesco Locatello
7. Bernhard Schölkopf
8. J. Urgen Schmidhuber

In Representation & Self-Supervised Learning

1. Bernhard Schölkopf
2. Francesco Locatello
3. Stefan Bauer
4. Olivier Bachem
5. Gunnar Rätsch
6. Michael Tschannen
7. Sjoerd van Steenkiste
8. Mario Lucic

Citations in the corpus

• A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020, background): “There is for example a rich literature in disentangling pose from content in 3D objects and content from motion in videos or time series in general (Yang et al., 2015; Yingzhen and Mandt, 2018a; Hsieh et al., 2018; Fortuin et al., 2019; Deng et al., 2017; Goroshin et al., 2015).”
• Weakly-Supervised Disentanglement Without Compromises (2020, background): “…(Karaletsos et al., 2015; Whitney et al., 2016; Fraccaro et al., 2017; Denton & Birodkar, 2017; Hsu et al., 2017; Yingzhen & Mandt, 2018; Locatello et al., 2018) and disentangling motion and pose from content (Hsieh et al., 2018; Fortuin et al., 2019; Deng et al., 2017; Goroshin et al., 2015).”
• On the Fairness of Disentangled Representations (2019, background): “Disentangling pose from content and content from motion are also classical computer vision problems that have been tackled with various degrees of supervision and inductive bias [92, 93, 40, 25, 18, 31, 42].”
• Are Disentangled Representations Helpful for Abstract Visual Reasoning? (2019, background): “Disentangling pose, style, or motion from content are classical vision tasks that has been studied with different degrees of supervision [70, 76, 77, 34, 19, 14, 21, 36].”
• Disentangled Sequential Autoencoder (2018, methodology): “Structured (Johnson et al., 2016) and Fac-torised VAEs (Deng et al., 2017) used hierarchical priors to learn more interpretable latent variables.”
• Disentangling Factors of Variation Using Few Labels (2019, background): “There is for example a rich literature in disentangling pose from content in 3D objects and content from motion in videos [64, 65, 24, 15, 12, 18, 26].”
• Deep Models of Interactions Across Sets (2018, methodology): “This approach builds on recent work applying convolutional neural networks to graph-structured data (Scarselli et al., 2009; Bruna et al., 2014; Duvenaud et al., 2015; Def-ferrard et al., 2016; Kipf & Welling, 2016; Hamilton et al., 2017).”
• Dynamical Variational Autoencoders: A Comprehensive Review (2020, methodology): “FVAE: The authors of (Deng et al., 2017) propose a model called Factorized VAE (FVAE) that combines the VAE with tensor factorization (Kuleshov et al.”
• Advances in Variational Inference (2017, unknown): “Recent work aims at using inter-pretable probabilistic models as priors for VAEs [38], [77], [91], [168].”