Enhancing Generative Models with Approximated Optimal Transport

Основні поняття

The author introduces the Approximated Optimal Transport (AOT) technique to improve diffusion-based generative models by integrating optimal transport into the training process, resulting in superior image quality and reduced sampling steps. The core thesis is that AOT enhances the performance of diffusion models by reducing curvature in ODE trajectories.

Анотація

The content discusses the introduction of the Approximated Optimal Transport (AOT) technique to enhance diffusion-based generative models. By approximating optimal transport and integrating it into the training process, the models achieve lower curvature in ODE trajectories, leading to improved image quality and reduced sampling steps. The study compares results between traditional diffusion models and those trained with AOT, showcasing significant improvements in FID scores and NFEs. Additionally, the incorporation of AOT in Discriminator Guidance (DG) further boosts model performance. The content provides detailed insights into the methodology, experiments, results, and implications for future research.

• Diffusion models synthesize images through progressive denoising.
• Score functions are crucial for image synthesis in diffusion models.
• EDM introduces advancements for high-quality image synthesis.
• AOT improves model performance by reducing ODE trajectory curvature.
• Sampling hyperparameters impact model performance significantly.
• Incorporating AOT in DG leads to state-of-the-art FID scores.

Статистика

Specifically, we achieve FID scores of 1.88 with just 27 NFEs and 1.73 with 29 NFEs in unconditional and conditional generations, respectively.
Furthermore, when applying AOT to train the discriminator for guidance, we establish new state-of-the-art FID scores of 1.68 and 1.58 for unconditional and conditional generations, respectively, each with 29 NFEs.

Цитати

"We introduce the Approximated Optimal Transport (AOT) technique."
"Our approach aims to approximate and integrate optimal transport into the training process."

Ключові висновки, отримані з

Improving Diffusion-Based Generative Models via Approximated Optimal Transport

by Daegyu Kim,J... о arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05069.pdf

Глибші Запити

How does incorporating AOT impact computational efficiency compared to traditional methods

Incorporating Approximated Optimal Transport (AOT) into diffusion models has a significant impact on computational efficiency compared to traditional methods. AOT allows for the approximation of optimal transport between distributions at the batch level, reducing the complexity of computing optimal transport across all time steps. By selecting pairs with minimal cost functions in each iteration and using the Hungarian algorithm to find optimal matches, AOT streamlines the process of pairing images and noise during training. This targeted approach reduces information entropy in high-noise scenarios, leading to straighter trajectories in ODEs and lower truncation errors during sampling. As a result, diffusion models trained with AOT exhibit improved performance with fewer sampling steps while maintaining high-quality image generation.

What potential applications beyond image generation could benefit from AOT techniques

Beyond image generation, AOT techniques have potential applications in various fields that involve data synthesis or transformation processes. One area that could benefit from AOT is natural language processing (NLP), particularly in text-to-image synthesis tasks where aligning textual descriptions with visual content is crucial. By leveraging AOT principles to optimize mappings between text embeddings and image features, NLP models can generate more accurate and coherent visual representations based on textual input. Additionally, applications in healthcare such as medical image analysis could utilize AOT for enhancing data transformations between different modalities or resolutions, improving diagnostic accuracy and treatment planning.

How might other generative modeling approaches benefit from integrating optimal transport principles

Integrating optimal transport principles into other generative modeling approaches can offer several benefits by addressing challenges related to distribution alignment and trajectory optimization. For instance:

In Variational Autoencoders (VAEs), incorporating optimal transport can improve latent space mapping by minimizing discrepancies between prior and posterior distributions.
GANs can leverage optimal transport for better mode coverage during training, leading to more diverse generated samples.
Flow-based generative models can enhance their invertibility properties through optimized coupling layers based on transportation costs.
By integrating optimal transport principles into these approaches, researchers can achieve more stable training dynamics, improved sample quality diversity, and enhanced model interpretability across various domains beyond just image generation tasks.