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Unsupervised Image Decomposition with Phase-Correlation Networks
[article]
2022
arXiv
pre-print
The ability to decompose scenes into their object components is a desired property for autonomous agents, allowing them to reason and act in their surroundings. Recently, different methods have been proposed to learn object-centric representations from data in an unsupervised manner. These methods often rely on latent representations learned by deep neural networks, hence requiring high computational costs and large amounts of curated data. Such models are also difficult to interpret. To
arXiv:2110.03473v3
fatcat:n6zalqjazbgingx2pi3itdgneq