232 citations · 342 across the 15 of their papers we have counts for
15 papers
Position: Fairness Failure in Generative Models is an Evaluation Problem
Mariia Vladimirova, Jean-Yves Franceschi, Thibaut Issenhuth
Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized g…
On the MIA Vulnerability Gap Between Private GANs and Diffusion Models
Ilana Sebag, Jean-Yves Franceschi, Alain Rakotomamonjy +2
Generative Adversarial Networks (GANs) and diffusion models have emerged as leading approaches for high-quality image synthesis. While both can be trained under differential privac…
Fused-Planes: Why Train a Thousand Tri-Planes When You Can Share?
Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi +5
Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar structures. This has made them the prevailing choice t…
Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder
Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi +5
While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides red…
Improving Consistency Models with Generator-Augmented Flows
Thibaut Issenhuth, Sangchul Lee, Ludovic Dos Santos +3
Consistency models imitate the multi-step sampling of score-based diffusion in a single forward pass of a neural network. They can be learned in two ways: consistency distillation…
Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi +5
We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes. We combine two techniques to improve the required training time and memory…
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi +4
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling. This can be achieved through either differentially private stochas…
RefinedFields: Radiance Fields Refinement for Planar Scene Representations
Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi +3
Planar scene representations have recently witnessed increased interests for modeling scenes from images, as their lightweight planar structure enables compatibility with image-bas…
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos +4
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle…
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi +2
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather predict…
A Neural Tangent Kernel Perspective of GANs
Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed +3
We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs). We reveal a fundamental flaw of previous analyses which, by incorrectly modeling GA…
PDE-Driven Spatiotemporal Disentanglement
Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier +1
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 differen…
Stochastic Latent Residual Video Prediction
Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen +2
Designing video prediction models that account for the inherent uncertainty of the future is challenging. Most works in the literature are based on stochastic image-autoregressive…
Unsupervised Scalable Representation Learning for Multivariate Time Series
Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice. In this paper, we tackle this…
Robustness of classifiers to uniform and Gaussian noise
Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi
We study the robustness of classifiers to various kinds of random noise models. In particular, we consider noise drawn uniformly from the ball for and…