Hierarchical Disentangled Representations

By Babak Esmaeili, Hao Wu, Sarthak Jain, N. Siddharth, Brooks Paige, Jan-Willem van de Meent. 2018

22 citations recorded by the source corpus.

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Citations in the corpus

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (2019, background): “Using some sort of supervision [e.g. 43, 4, 35, 40, 9] or proposing completely unsupervised [e.g. 19, 24, 7, 27, 13] learning approaches.”
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness (2018, background): “…latent dimensions and generative factors provides more ﬂexibility, for example when multiple latent dimensions are used to describe one phenomenon (Esmaeili et al., 2018) or when some sort of supervision is available through groupings in the dataset according to generative factors (Bouchacourt et…”
Disentangling Disentanglement in Variational Autoencoders (2018, methodology): “There have also been a number of approaches that decompose the standard VAE objective in different ways (e.g. Dilok-thanakul et al., 2019; Esmaeili et al., 2019; Hoffman and Johnson, 2016) to expose KL ( q φ ( z ) (cid:107) p ( z )) as a component, but, as we discuss in Appendix C, this can be…”
Unsupervised Discovery of Parts, Structure, and Dynamics (2019, background): “There have been also extensive research on hierarchical motion decomposition (Ross & Zemel, 2006; Ross et al., 2010; Grundmann et al., 2010; Xu et al., 2012; Flores-Mangas & Jepson, 2013; Jain et al., 2014; Ochs et al., 2014; Pérez-Rúa et al., 2016; Gershman et al., 2016; Esmaeili et al., 2018).”
SOM-VAE: Interpretable Discrete Representation Learning on Time Series (2019, background): “A lot of work has been done to improve them in this regard, in GANs (Chen et al., 2016) as well as VAEs (Higgins et al., 2017; Esmaeili et al., 2018).”
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series (2018, background): “A lot of work has been done to improve them in this regard, in GANs [6] as well as VAEs [9, 16].”