Nvidia corporation (20240161250). TECHNIQUES FOR DENOISING DIFFUSION USING AN ENSEMBLE OF EXPERT DENOISERS simplified abstract

TECHNIQUES FOR DENOISING DIFFUSION USING AN ENSEMBLE OF EXPERT DENOISERS

Organization Name

nvidia corporation

Inventor(s)

Yogesh Balaji of Mountain View CA (US)

Timo Oskari Aila of Tuusula (FI)

Miika Aittala of Tuusula (FI)

Bryan Catanzaro of Los Altos Hills CA (US)

Xun Huang of Mountain View CA (US)

Tero Tapani Karras of Helsinki (FI)

Karsten Kreis of Vancouver (CA)

Samuli Laine of Vantaa (FI)

Ming-Yu Liu of San Jose CA (US)

Seungjun Nah of Santa Clara CA (US)

Jiaming Song of San Carlos CA (US)

Arash Vahdat of San Mateo CA (US)

Qinsheng Zhang of Santa Clara CA (US)

TECHNIQUES FOR DENOISING DIFFUSION USING AN ENSEMBLE OF EXPERT DENOISERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240161250 titled 'TECHNIQUES FOR DENOISING DIFFUSION USING AN ENSEMBLE OF EXPERT DENOISERS

Simplified Explanation

The techniques disclosed in this patent application involve generating a content item through denoising operations using machine learning models.

• Denoising operations are performed based on an input and a machine learning model to generate a content item.
• Multiple denoising operations are conducted based on the input, the generated content item, and a second machine learning model to produce a refined content item.
• The first machine learning model is trained to denoise content items with corruption within a specific range, while the second machine learning model is trained for a lower corruption range.

Potential Applications

The technology described in this patent application could be applied in various fields such as image processing, video editing, audio enhancement, and data cleaning.

Problems Solved

This technology addresses the challenge of removing noise and corruption from content items, resulting in cleaner and more accurate data for analysis and presentation.

Benefits

The benefits of this technology include improved data quality, enhanced visualization, increased accuracy in machine learning models, and overall better user experience.

Potential Commercial Applications

One potential commercial application of this technology could be in the development of software tools for content creators, data scientists, and multimedia professionals to enhance the quality of their work.

Possible Prior Art

One possible prior art for this technology could be existing denoising algorithms and machine learning models used in image and signal processing applications.

How does this technology compare to traditional denoising methods?

This article does not provide a direct comparison between the proposed technology and traditional denoising methods. It would be helpful to understand the specific advantages and limitations of this approach in comparison to existing techniques.

What are the computational requirements for implementing this technology?

The article does not delve into the computational resources needed to deploy this technology. Understanding the computational complexity and hardware specifications required could be crucial for practical applications.

Original Abstract Submitted

techniques are disclosed herein for generating a content item. the techniques include performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item, and performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item, where the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.