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Found 8 papers, 2 papers with code
Learning disconnected manifolds: a no GAN's land
Typical architectures of Generative Adversarial Networks make use of a unimodal latent/input distribution transformed by a continuous generator.
Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes.
RefinedFields: Radiance Fields Refinement for Unconstrained Scenes
We carry out extensive experiments and verify the merit of our method on synthetic data and real tourism photo collections.
Latent reweighting, an almost free improvement for GANs
Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting different classes of images.
Learning Disconnected Manifolds: Avoiding The No Gan's Land by Latent Rejection
Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting disconnected manifolds.
Learning disconnected manifolds: a no GANs land
Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator.
End-to-end optimization of goal-driven and visually grounded dialogue systems
End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning.
Hybrid Collaborative Filtering with Autoencoders
Such algorithms look for latent variables in a large sparse matrix of ratings.
