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Found 13 papers, 8 papers with code
Fused-Planes: Improving Planar Representations for Learning Large Sets of 3D Scenes
In this paper, we introduce Fused-Planes, a new planar architecture that improves Tri-Planes resource-efficiency in the framework of learning large sets of scenes, which we call "multi-scene inverse graphics".
Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder
To this end, we regularize an image autoencoder with 3D-geometry by aligning its latent space with jointly trained latent 3D scenes.
Improving Consistency Models with Generator-Induced Flows
The former relies on the true velocity field of the corresponding differential equation, approximated by a pre-trained neural network.
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.
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
In this paper, we introduce a novel differentially private generative modeling approach based on a gradient flow in the space of probability measures.
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.
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance.
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations.
A Neural Tangent Kernel Perspective of GANs
We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs).
PDE-Driven Spatiotemporal Disentanglement
A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory.
Stochastic Latent Residual Video Prediction
Designing video prediction models that account for the inherent uncertainty of the future is challenging.
Ranked #1 on
Video Prediction
on Cityscapes 128x128
(Pred metric)
Unsupervised Scalable Representation Learning for Multivariate Time Series
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice.
Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise
We study the robustness of classifiers to various kinds of random noise models.
