Group-based Learning of Disentangled Representations with Generalizability for Novel Contents

By Haruo Hosoya. 2019

55 citations recorded by the source corpus.

Sensory data are often comprised of independent content and transformation factors. For example, face images may have shapes as content and poses as transformation. To infer separately these factors from given data, various ``disentangling'' models have been proposed. However, many of these are supervised or semi-supervised, either requiring attribute labels that are often unavailable or disallowing for generalization over new contents. In this study, we introduce a novel deep generative model, called group-based variational autoencoders. In this, we assume no explicit labels, but a weaker form of structure that groups together data instances having the same content but transformed differently; we thereby separately estimate a group-common factor as content and an instance-specific factor as transformation. This approach allows for learning to represent a general continuous space of contents, which can accommodate unseen contents. Despite the simplicity, our model succeeded in learning, from five datasets, content representations that are highly separate from the transformation representation and generalizable to data with novel contents. We further provide detailed analysis of the latent content code and show insight into how our model obtains the notable transformation invariance and content generalizability.

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

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Citations in the corpus

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020, background): “, 2017), (ii) when weak forms of supervision like grouping information are available (Bouchacourt et al., 2018; Shu et al., 2020; Hosoya, 2019; Locatello et al., 2020a), or (iii) when temporal structure is available for the learning problem (Locatello et al.”
Weakly-Supervised Disentanglement Without Compromises (2020, methodology): “The case k = 1 where we actually know which factor of variation is not shared was previously considered in (Bouchacourt et al., 2018; Hosoya, 2019; Shu et al., 2020).”
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style (2021, background): “2 for generative SSL is of potential relevance for existing variational autoencoder (VAE) [68] variants such as the GroupVAE [51],9 or its adaptive version AdaGVAE [83].”