Competitive Training of Mixtures of Independent Deep Generative Models

By Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf. 2018

31 citations recorded by the source corpus.

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In Generative Models

Citations in the corpus

Object-Centric Learning with Slot Attention (2020, background): “[65] Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Rätsch, Sylvain Gelly, and Bernhard Schölkopf.”
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020, background): “One can also exploit known relations between factors in different samples (Karaletsos et al., 2015; Goroshin et al., 2015; Whitney et al., 2016; Fraccaro et al., 2017; Denton and Birodkar, 2017; Hsu et al., 2017; Yingzhen and Mandt, 2018b; Locatello et al., 2018).”
Towards causal generative scene models via competition of experts (2020, background): “…learning (Aljundi et al., 2017), learning independent causal mechanisms (Parascandolo et al., 2018), training mixtures of generative models (Locatello et al., 2018), as well as to dynamical systems via sparsely-interacting recurrent independent mechanisms (Goyal et al., 2019). and a second…”
Weakly-Supervised Disentanglement Without Compromises (2020, background): “For example, a number of works rely on known relations between the factors of variation (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 (Hs”
Causality for Machine Learning (2019, background): “A sensible inductive bias to learn such models is to look for independent causal mechanisms (Locatello et al., 2018b), and competitive training can play a role in this: for a pattern recognition task, Parascandolo et al. (2018) show that learning causal models that contain independent mechanisms…”
On the Fairness of Disentangled Representations (2019, methodology): “Weaker forms of supervision like relational information or additional assumptions on the effect of the factors of variation were also studied [39, 16, 47, 31, 91, 26, 19, 41, 93, 62, 53, 81, 8] and applied in the sequential data and reinforcement learning settings [88, 85, 57, 69, 37, 38].”
Are Disentangled Representations Helpful for Abstract Visual Reasoning? (2019, background): “Due to the initial poor performance of purely unsupervised methods, the field initially focused on semi-supervised [62, 11, 57, 58, 44, 46] and weakly supervised approaches [31, 12, 40, 21, 78, 20, 15, 35, 80, 54, 47, 64, 8].”
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018, background): “, 2017; Yingzhen & Mandt, 2018) or explicit inductive biases (Locatello et al., 2018).”
Disentangling Factors of Variation Using Few Labels (2019, background): “[43] Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar Rätsch, Sylvain Gelly, and Bernhard Schölkopf.”
SOM-VAE: Interpretable Discrete Representation Learning on Time Series (2019, methodology): “This includes methods that perform clustering in the latent space of (variational) autoencoders (Aljalbout et al., 2018) or use a mixture of autoencoders for the clustering (Zhang et al., 2017; Locatello et al., 2018).”