The key idea behind the unsupervised learning of disentangled representations is that real- world data is generated by a few explanatory factors of variation which can be recovered by …
K Do, T Tran - arXiv preprint arXiv:1908.09961, 2019 - arxiv.org
We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for …
Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al.(2019) demonstrated that unsupervised …
A large part of the literature on learning disentangled representations focuses on variational autoencoders (VAEs). Recent developments demonstrate that disentanglement cannot be …
H Kim, A Mnih - International conference on machine …, 2018 - proceedings.mlr.press
We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose …
How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from …
Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant …
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns …
MA Carbonneau, J Zaidi, J Boilard… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Learning to disentangle and represent factors of variation in data is an important problem in artificial intelligence. While many advances have been made to learn these representations …