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Casimir JH Ludwig

2 accepted papers

2022

Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation

NeurIPS 2022accept

Recent research has shown that generative models with highly disentangled representations fail to generalise to unseen combination of generative factor values. These findings contradict earlier research which showed improved performance in out-of-training distribution settings when compared to entan…

Cited by 27SourcePDFScholar
2021

The role of Disentanglement in Generalisation

ICLR 2021poster

Combinatorial generalisation — the ability to understand and produce novel combinations of familiar elements — is a core capacity of human intelligence that current AI systems struggle with. Recently, it has been suggested that learning disentangled representations may help address this problem. It…

Cited by 118SourcePDFScholar