View source for 18485239. TECHNIQUES FOR DENOISING DIFFUSION USING AN ENSEMBLE OF EXPERT DENOISERS simplified abstract (NVIDIA Corporation)

=TECHNIQUES FOR DENOISING DIFFUSION USING AN ENSEMBLE OF EXPERT DENOISERS=


==Organization Name==

[[:Category:NVIDIA Corporation|NVIDIA Corporation]]


[[Category:NVIDIA Corporation]]

==Inventor(s)==

[[:Category:Yogesh Balaji of Mountain View CA (US)|Yogesh Balaji of Mountain View CA (US)]][[Category:Yogesh Balaji of Mountain View CA (US)]]

[[:Category:Timo Oskari Aila of Tuusula (FI)|Timo Oskari Aila of Tuusula (FI)]][[Category:Timo Oskari Aila of Tuusula (FI)]]

[[:Category:Miika Aittala of Tuusula (FI)|Miika Aittala of Tuusula (FI)]][[Category:Miika Aittala of Tuusula (FI)]]

[[:Category:Bryan Catanzaro of Los Altos Hills CA (US)|Bryan Catanzaro of Los Altos Hills CA (US)]][[Category:Bryan Catanzaro of Los Altos Hills CA (US)]]

[[:Category:Xun Huang of Mountain View CA (US)|Xun Huang of Mountain View CA (US)]][[Category:Xun Huang of Mountain View CA (US)]]

[[:Category:Tero Tapani Karras of Helsinki (FI)|Tero Tapani Karras of Helsinki (FI)]][[Category:Tero Tapani Karras of Helsinki (FI)]]

[[:Category:Karsten Kreis of Vancouver (CA)|Karsten Kreis of Vancouver (CA)]][[Category:Karsten Kreis of Vancouver (CA)]]

[[:Category:Samuli Laine of Vantaa (FI)|Samuli Laine of Vantaa (FI)]][[Category:Samuli Laine of Vantaa (FI)]]

[[:Category:Ming-Yu Liu of San Jose CA (US)|Ming-Yu Liu of San Jose CA (US)]][[Category:Ming-Yu Liu of San Jose CA (US)]]

[[:Category:Seungjun Nah of Santa Clara CA (US)|Seungjun Nah of Santa Clara CA (US)]][[Category:Seungjun Nah of Santa Clara CA (US)]]

[[:Category:Jiaming Song of San Carlos CA (US)|Jiaming Song of San Carlos CA (US)]][[Category:Jiaming Song of San Carlos CA (US)]]

[[:Category:Arash Vahdat of San Mateo CA (US)|Arash Vahdat of San Mateo CA (US)]][[Category:Arash Vahdat of San Mateo CA (US)]]

[[:Category:Qinsheng Zhang of Santa Clara CA (US)|Qinsheng Zhang of Santa Clara CA (US)]][[Category: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 18485239 titled 'TECHNIQUES FOR DENOISING DIFFUSION USING AN ENSEMBLE OF EXPERT DENOISERS

==Simplified Explanation==

The abstract describes techniques for generating a content item using denoising operations and machine learning models. The first model is trained to denoise content with a certain level of corruption, while the second model is trained for a lower level of corruption.

* The techniques involve performing denoising operations based on input and machine learning models.
* The first model generates a content item within a specific corruption range.
* The second model refines the content item further within a lower corruption range.

== Potential Applications ==
The technology can be applied in various fields such as image processing, video editing, and audio enhancement.

== Problems Solved ==
1. Improving the quality of content items by removing noise and corruption.
2. Enhancing the accuracy and reliability of machine learning models in denoising tasks.

== Benefits ==
1. Higher quality content generation.
2. Improved performance of machine learning models.
3. Enhanced user experience in multimedia applications.

== Potential Commercial Applications ==
"Content Generation Techniques Using Denoising Operations and Machine Learning Models" can be utilized in industries such as entertainment, advertising, and digital marketing for creating high-quality multimedia content.

== Possible Prior Art ==
One possible prior art could be the use of denoising algorithms in image processing applications to enhance image quality and reduce noise.

=== What are the limitations of the denoising operations used in this technology? ===

The limitations of the denoising operations may include:
1. Inability to handle extremely high levels of corruption.
2. Potential loss of detail or information during the denoising process.

=== How does the second machine learning model improve upon the first model in terms of denoising content items? ===

The second machine learning model is trained to denoise content items with a lower corruption range, allowing for further refinement and enhancement of the generated content items compared to the first model.


==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.



[[Category:G06T5/00]]