Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations

By Xander Steenbrugge, Sam Leroux, Tim Verbelen, Bart Dhoedt. 2018

72 citations recorded by the source corpus.

In this work we explore the generalization characteristics of unsupervised representation learning by leveraging disentangled VAE's to learn a useful latent space on a set of relational reasoning problems derived from Raven Progressive Matrices. We show that the latent representations, learned by unsupervised training using the right objective function, significantly outperform the same architectures trained with purely supervised learning, especially when it comes to generalization.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

In Representation & Self-Supervised Learning

Citations in the corpus

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020, methodology): “While prior work (Steenbrugge et al., 2018; LaversanneFinot et al., 2018; Nair et al., 2018; Higgins et al., 2017b, 2018b) successfully applied disentanglement methods such as β-VAE on a variety of downstream tasks, it is not clear to us that these approaches and trained models performed well…”
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (2019, background): “sequence modeling, curiosity-based exploration or even domain adaptation in reinforcement learning [45, 25, 37, 16, 18, 29, 47].”
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): “Disentangled representations have been found to be more sample-efficient [29], less sensitive to nuisance variables [55], and better in terms of (systematic) generalization [1, 16, 28, 35, 70].”
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018, methodology): “While prior work (Steenbrugge et al., 2018; Laversanne-Finot et al., 2018; Nair et al., 2018; Higgins et al., 2017b; 2018) successfully applied disentanglement methods such as β-VAE on a variety of downstream tasks, it is not clear to us that these approaches and trained models performed well becaus”
Disentangling Factors of Variation Using Few Labels (2019, background): “[59] Xander Steenbrugge, Sam Leroux, Tim Verbelen, and Bart Dhoedt.”
Unsupervised Model Selection for Variational Disentangled Representation Learning (2019, background): “…have demonstrated that models with disentangled representations show improvements in terms of these shortcomings (Higgins et al., 2017b; 2018b; Achille et al., 2018; Steenbrugge et al., 2018; Nair et al., 2018; Laversanne-Finot et al., 2018; van Steenkiste et al., 2019; Locatello et al., 2019).”
Selective Replay Enhances Learning in Online Continual Analogical Reasoning (2021, methodology): “Performance has been further improved by augmenting WReN with an unsupervised disentangled variational autoencoder [78] and Transformer attention mechanisms [32].”
Stratified Rule-Aware Network for Abstract Visual Reasoning (2020, unknown): “…been some success with end-to-end learning methods trying to accomplish abstract reasoning on RPM test (Hoshen and Werman 2017; Barrett et al. 2018; Steenbrugge et al. 2018; Zhang et al. 2019a,b; Zheng, Zha, and Wei 2019; van Steenkiste et al. 2019; Wang, Jamnik, and Lio 2020), inspired by the…”
Generalization to New Actions in Reinforcement Learning (2020, unknown): “Prior work has explored downstream tasks such as classification and video prediction (Denton & Birodkar, 2017), relational reasoning through visual representation of objects (Steenbrugge et al., 2018), domain adaptation in RL by representing image states (Higgins et al.”