Core Concepts
Diffusion process improves adversarial EBMs by splitting the generation process into smaller steps, addressing training challenges and enhancing generation quality.
Abstract
Abstract:
Generative models focus on strong generation ability.
Energy-based models (EBMs) efficiently parameterize unnormalized densities.
Adversarial EBMs introduce a generator to avoid MCMC sampling.
Diffusion-based models inspire the integration of EBMs into denoising steps.
Introduction:
EBMs define unnormalized probability distributions.
Training EBMs using MLE can be challenging due to the lack of a closed-form expression for the normalization constant.
Adversarial EBMs introduce a minimax game between a generator and energy function.
Limitations of adversarial EBMs include instability in training and reliance on KL divergence.
Denoising Diffusion Adversarial EBM:
Adversarial EBMs integrated into denoising diffusion process.
Conditional denoising distributions optimized to alleviate training burden.
Symmetric Jeffrey divergence and variational posterior distribution introduced for training.
Experiments:
Evaluation on 2D synthetic data, image generation, and out-of-distribution detection.
Performance on CIFAR-10 dataset with FID 4.82 and IS 8.86.
OOD detection using AUROC metric.
Ablation Studies:
Importance of proposed modifications such as latent variable, introduced posterior, and Jeffrey divergence.
Influence of varying the number of time steps on model performance.
Stats
Adversarial EBMs avoid MCMC by introducing a variational distribution pϕ to approximate pθ.
Adversarial EBMs introduce a minimax game to alternately optimize two adversarial steps.
Quotes
"Our experiments show significant improvement in generation compared to existing adversarial EBMs."
"We propose an MCMC-free training framework for EBMs to incorporate a sequence of adversarial EBMs into a denoising diffusion process."