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Found 15 papers, 11 papers with code
On Relativistic f-Divergences
We take a more rigorous look at Relativistic Generative Adversarial Networks (RGANs) and prove that the objective function of the discriminator is a statistical divergence for any concave function $f$ with minimal properties ($f(0)=0$, $f'(0) \neq 0$, $\sup_x f(x)>0$).
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
Through empirical evaluation across the benchmark, we demonstrate that our approach outperforms deep-learning generation methods in data generation tasks and remains competitive in data imputation.
Diffusion models with location-scale noise
Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it.
PopulAtion Parameter Averaging (PAPA)
Based on this idea, we propose PopulAtion Parameter Averaging (PAPA): a method that combines the generality of ensembling with the efficiency of weight averaging.
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback
We propose a novel regularizer for supervised learning called Conditioning on Noisy Targets (CNT).
MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation
We train the model in a manner where we randomly and independently mask all the past frames or all the future frames.
Ranked #4 on
Video Generation
on BAIR Robot Pushing
Gotta Go Fast with Score-Based Generative Models
Score-based (denoising diffusion) generative models have recently gained a lot of success in generating realistic and diverse data.
Gotta Go Fast When Generating Data with Score-Based Models
For high-resolution images, our method leads to significantly higher quality samples than all other methods tested.
Ranked #8 on
Image Generation
on CIFAR-10
(Inception score metric)
Adversarial score matching and improved sampling for image generation
Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently found success in generative modeling.
Ranked #54 on
Image Generation
on CIFAR-10
Stochastic Hamiltonian Gradient Methods for Smooth Games
The success of adversarial formulations in machine learning has brought renewed motivation for smooth games.
Gradient penalty from a maximum margin perspective
We present a unifying framework of expected margin maximization and show that a wide range of gradient-penalized GANs (e. g., Wasserstein, Standard, Least-Squares, and Hinge GANs) can be derived from this framework.
Ranked #131 on
Image Generation
on CIFAR-10
On Relativistic $f$-Divergences
Given the good performance of RGANs, this suggests that WGAN does not performs well primarily because of the weak metric, but rather because of regularization and the use of a relativistic discriminator.
GANs beyond divergence minimization
We observe that most loss functions converge well and provide comparable data generation quality to non-saturating GAN, LSGAN, and WGAN-GP generator loss functions, whether we use divergences or non-divergences.
The relativistic discriminator: a key element missing from standard GAN
We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.
Ranked #2 on
Image Generation
on CAT 256x256
Alternating optimization for GxE modelling with weighted genetic and environmental scores: examples from the MAVAN study
The approach uses alternating optimization to estimate the parameters of the GxE model.
Applications
