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• W3040956815 abstract "A range of methods with suitable inductive biases exist to learn interpretable object-centric representations of images without supervision. However, these are largely restricted to visually simple images; robust object discovery in real-world sensory datasets remains elusive. To increase the understanding of such inductive biases, we empirically investigate the role of for scene decomposition in GENESIS, a recent VAE-based model. We show such bottlenecks determine reconstruction and segmentation quality and critically influence model behaviour." @default.
• W3040956815 created "2020-07-16" @default.
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• W3040956815 date "2020-07-13" @default.
• W3040956815 modified "2023-09-23" @default.
• W3040956815 title "Reconstruction Bottlenecks in Object-Centric Generative Models." @default.
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• W3040956815 hasPublicationYear "2020" @default.
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