Semi-Implicit Denoising Diffusion Models (SIDDMs).

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Denoising Diffusion Probabilistic Models (DDPM) generate high-quality samples but are slow due to many iterative steps.
• Denoising Diffusion Generative Adversarial Networks (DDGAN) aimed for faster sampling but faced scalability issues with large datasets.

Purpose of the Study:

• To develop a novel generative model that achieves fast sampling during inference without sacrificing sample diversity and quality.
• To address the limitations of existing diffusion models and GAN-based approaches in terms of speed and scalability.

Main Methods:

• We propose a new approach that matches implicit and explicit factors by using an implicit model to align noisy data marginal distributions with the forward diffusion's explicit conditional distribution.
• This method effectively matches joint denoising distributions, allowing large steps during inference by not enforcing a parametric distribution for the reverse step, similar to DDGAN.
• The approach leverages the exact form of the diffusion process, akin to DDPM, ensuring generative performance.

Main Results:

• The proposed method achieves generative performance comparable to existing diffusion-based models.
• It demonstrates significantly superior results compared to models employing a small number of sampling steps.
• The approach offers a balance between sampling speed and generative quality.

Conclusions:

• Our novel method provides an effective solution for fast and high-quality generative sampling.
• It overcomes the limitations of previous diffusion and GAN-based models, offering improved scalability and efficiency.
• The presented technique represents a significant advancement in generative modeling, particularly for applications requiring rapid inference.